Solves 500 Errors For Some Users

.gitattributes

@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
@@ -33,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+scale-hf-logo.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1 +1,13 @@
-__pycache__
+auto_evals/
+venv/
+__pycache__/
+.env
+.ipynb_checkpoints
+*ipynb
+.vscode/
+
+eval-queue/
+eval-results/
+eval-queue-bk/
+eval-results-bk/
+logs/

.pre-commit-config.yaml

@@ -0,0 +1,53 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+default_language_version:
+  python: python3
+
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+  autoupdate_schedule: quarterly
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: check-yaml
+      - id: check-case-conflict
+      - id: detect-private-key
+      - id: check-added-large-files
+        args: ['--maxkb=1000']
+      - id: requirements-txt-fixer
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        name: Format imports
+
+  - repo: https://github.com/psf/black
+    rev: 22.12.0
+    hooks:
+      - id: black
+        name: Format code
+        additional_dependencies: ['click==8.0.2']
+
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    # Ruff version.
+    rev: 'v0.0.267'
+    hooks:
+      - id: ruff

.streamlit/config.toml

@@ -1,2 +0,0 @@
-[theme]
-base = "light"

Dockerfile

@@ -1,21 +0,0 @@
-FROM python:3.13.5-slim
-
-RUN useradd -m -u 1000 user
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY --chown=user ./requirements.txt requirements.txt
-COPY --chown=user . /app
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "fev-leaderboard-app.py", "--server.port=8501", "--server.address=0.0.0.0"]

Makefile

@@ -0,0 +1,13 @@
+.PHONY: style format
+
+
+style:
+	python -m black --line-length 119 .
+	python -m isort .
+	ruff check --fix .
+
+
+quality:
+	python -m black --check --line-length 119 .
+	python -m isort --check-only .
+	ruff check .

README.md

@@ -1,20 +1,44 @@
 ---
-title: fev-bench
-emoji: 🏆
+title: Fev Leaderboard
+emoji: 🥇
 colorFrom: green
 colorTo: indigo
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Forecast evaluation benchmark
+sdk: gradio
+app_file: app.py
+pinned: true
 license: apache-2.0
 ---
 
-# Welcome to Streamlit!
+# Start the configuration
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+Most of the variables to change for a default leaderboard are in `src/env.py` (replace the path for your leaderboard) and `src/about.py` (for tasks).
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+Results files should have the following format and be stored as json files:
+```json
+{
+    "config": {
+        "model_dtype": "torch.float16", # or torch.bfloat16 or 8bit or 4bit
+        "model_name": "path of the model on the hub: org/model",
+        "model_sha": "revision on the hub",
+    },
+    "results": {
+        "task_name": {
+            "metric_name": score,
+        },
+        "task_name2": {
+            "metric_name": score,
+        }
+    }
+}
+```
+
+Request files are created automatically by this tool.
+
+If you encounter problem on the space, don't hesitate to restart it to remove the create eval-queue, eval-queue-bk, eval-results and eval-results-bk created folder.
+
+# Code logic for more complex edits
+
+You'll find 
+- the main table' columns names and properties in `src/display/utils.py`
+- the logic to read all results and request files, then convert them in dataframe lines, in `src/leaderboard/read_evals.py`, and `src/populate.py`
+- the logic to allow or filter submissions in `src/submission/submit.py` and `src/submission/check_validity.py`

pages/chronos_bench_ii.py → app.py

@@ -1,41 +1,18 @@
-import sys
-from pathlib import Path
-
-sys.path.append(str(Path(__file__).parent.parent))
-
 import fev
+import gradio as gr
 import pandas as pd
-import streamlit as st
-from streamlit.elements.lib.column_types import ColumnConfig
-
-from src.strings import (
-    CHRONOS_BENCHMARK_BASIC_INFO,
-    CHRONOS_BENCHMARK_DETAILS,
-    CITATION_CHRONOS,
-    CITATION_FEV,
-    CITATION_HEADER,
-    PAIRWISE_BENCHMARK_DETAILS,
-    get_pivot_legend,
-)
-from src.utils import (
-    construct_bar_chart,
-    construct_pairwise_chart,
-    construct_pivot_table,
-    format_leaderboard,
-    format_metric_name,
-    get_metric_description,
+
+from src import about
+from src.custom_html_js import custom_css
+from src.formatting import make_clickable_model
+
+# Load the CSV data into a pandas DataFrame
+df = pd.read_csv(
+    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/seasonal_naive.csv"
 )
 
-st.set_page_config(layout="wide", page_title="FEV Benchmark Leaderboard", page_icon=":material/trophy:")
 
-TITLE = "<h1 style='text-align: center; font-size: 350%;'>Chronos Benchmark II</h1>"
-BASELINE_MODEL = "seasonal_naive"
-LEAKAGE_IMPUTATION_MODEL = "chronos_bolt_base"
-SORT_COL = "win_rate"
-N_RESAMPLES_FOR_CI = 1000
-TOP_K_MODELS_TO_PLOT = 15
-AVAILABLE_METRICS = ["WQL", "MASE"]
-SUMMARY_URLS = [
+summary_urls = [
     "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/auto_arima.csv",
     "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/auto_ets.csv",
     "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/auto_theta.csv",
@@ -58,122 +35,63 @@ SUMMARY_URLS = [
     "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/tirex.csv",
 ]
 
-
-@st.cache_data()
-def load_summaries():
-    summaries = []
-    for url in SUMMARY_URLS:
-        df = pd.read_csv(url)
-        summaries.append(df)
-    return pd.concat(summaries, ignore_index=True)
-
-
-@st.cache_data()
-def get_leaderboard(metric_name: str) -> pd.DataFrame:
-    summaries = load_summaries()
-    lb = fev.analysis.leaderboard(
-        summaries=summaries,
-        metric_column=metric_name,
-        missing_strategy="impute",
-        baseline_model=BASELINE_MODEL,
-        leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
-    )
-    lb = lb.astype("float64").reset_index()
-
-    lb["skill_score"] = lb["skill_score"] * 100
-    lb["win_rate"] = lb["win_rate"] * 100
-    lb["num_failures"] = lb["num_failures"] / summaries["task_name"].nunique() * 100
-    return lb
-
-
-@st.cache_data()
-def get_pairwise(metric_name: str, included_models: list[str]) -> pd.DataFrame:
-    if BASELINE_MODEL not in included_models:
-        included_models = included_models + [BASELINE_MODEL]
-    summaries = load_summaries()
-    return (
-        fev.analysis.pairwise_comparison(
-            summaries,
-            included_models=included_models,
-            metric_column=metric_name,
-            baseline_model=BASELINE_MODEL,
-            missing_strategy="impute",
-            n_resamples=N_RESAMPLES_FOR_CI,
-            leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
-        )
-        .round(3)
-        .reset_index()
-    )
-
-
-with st.sidebar:
-    selected_metric = st.selectbox("Evaluation Metric", options=AVAILABLE_METRICS, format_func=format_metric_name)
-    st.caption(get_metric_description(selected_metric))
-
-cols = st.columns(spec=[0.025, 0.95, 0.025])
-
-with cols[1] as main_container:
-    st.markdown(TITLE, unsafe_allow_html=True)
-
-    metric_df = get_leaderboard(selected_metric).sort_values(by=SORT_COL, ascending=False)
-    top_k_models = metric_df.head(TOP_K_MODELS_TO_PLOT)["model_name"].tolist()
-    pairwise_df = get_pairwise(selected_metric, included_models=top_k_models)
-
-    st.markdown("## :material/trophy: Leaderboard", unsafe_allow_html=True)
-    st.markdown(CHRONOS_BENCHMARK_BASIC_INFO, unsafe_allow_html=True)
-    df_styled = format_leaderboard(metric_df)
-    st.dataframe(
-        df_styled,
-        use_container_width=True,
-        hide_index=True,
-        column_config={
-            "model_name": ColumnConfig(label="Model Name", alignment="left"),
-            "win_rate": st.column_config.NumberColumn(label="Avg. win rate (%)", format="%.1f"),
-            "skill_score": st.column_config.NumberColumn(label="Skill score (%)", format="%.1f"),
-            "median_inference_time_s": st.column_config.NumberColumn(label="Median runtime (s)", format="%.1f"),
-            "training_corpus_overlap": st.column_config.NumberColumn(label="Leakage (%)", format="%d"),
-            "num_failures": st.column_config.NumberColumn(label="Failed tasks (%)", format="%.0f"),
-            "zero_shot": ColumnConfig(label="Zero-shot", alignment="center"),
-            "org": ColumnConfig(label="Organization", alignment="left"),
-            "link": st.column_config.LinkColumn(label="Link", display_text=":material/open_in_new:"),
-        },
-    )
-
-    with st.expander("See details"):
-        st.markdown(CHRONOS_BENCHMARK_DETAILS, unsafe_allow_html=True)
-
-    st.markdown("## :material/bar_chart: Pairwise comparison", unsafe_allow_html=True)
-    chart_col_1, _, chart_col_2 = st.columns(spec=[0.45, 0.1, 0.45])
-
-    with chart_col_1:
-        st.altair_chart(
-            construct_pairwise_chart(pairwise_df, col="win_rate", metric_name=selected_metric),
-            use_container_width=True,
-        )
-
-    with chart_col_2:
-        st.altair_chart(
-            construct_pairwise_chart(pairwise_df, col="skill_score", metric_name=selected_metric),
-            use_container_width=True,
-        )
-
-    with st.expander("See details"):
-        st.markdown(PAIRWISE_BENCHMARK_DETAILS, unsafe_allow_html=True)
-
-    st.markdown("## :material/table_chart: Results for individual tasks", unsafe_allow_html=True)
-    with st.expander("Show detailed results"):
-        st.markdown(get_pivot_legend(BASELINE_MODEL, LEAKAGE_IMPUTATION_MODEL), unsafe_allow_html=True)
-        st.dataframe(
-            construct_pivot_table(
-                summaries=load_summaries(),
-                metric_name=selected_metric,
-                baseline_model=BASELINE_MODEL,
-                leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
-            )
-        )
-
-    st.divider()
-    st.markdown("### :material/format_quote: Citation", unsafe_allow_html=True)
-    st.markdown(CITATION_HEADER)
-    st.markdown(CITATION_FEV)
-    st.markdown(CITATION_CHRONOS)
+rename_cols = {
+    "gmean_relative_error": "Average relative error",
+    "avg_rank": "Average rank",
+    "median_inference_time_s": "Median inference time (s)",
+    "training_corpus_overlap": "Training corpus overlap (%)",
+}
+selected_cols = list(rename_cols.keys())
+
+
+def highlight_zeroshot(styler):
+    """Highlight training overlap for zero-shot models with bold green."""
+
+    def style_func(val):
+        if val == 0:
+            return "color: green; font-weight: bold"
+        else:
+            return "color: black"
+
+    return styler.map(style_func, subset=["Training corpus overlap (%)"])
+
+
+leaderboards = {}
+for metric in ["WQL", "MASE"]:
+    lb = fev.leaderboard(summary_urls, metric_column=metric)[selected_cols].rename(columns=rename_cols)
+    lb = lb.astype("float64").round(3).reset_index()
+    lb["Training corpus overlap (%)"] = (lb["Training corpus overlap (%)"] * 100).round(1)
+    lb["model_name"] = lb["model_name"].apply(make_clickable_model)
+    leaderboards[metric] = highlight_zeroshot(lb.style).format(precision=3)
+
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.HTML(about.TITLE)
+    gr.Markdown(about.INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons"):
+        with gr.Tab("🏅 Chronos Benchmark II", id=0):
+            with gr.Column():
+                gr.Markdown(about.CHRONOS_BENCHMARK, elem_classes="markdown-text")
+            with gr.Tabs():
+                with gr.Tab("📊 Probabilistic forecast (WQL)"):
+                    gr.Markdown("""Forecast accuracy measured by Weighted Quantile Loss.""")
+                    gr.Dataframe(
+                        value=leaderboards["WQL"],
+                        datatype=["markdown", "number", "number", "number"],
+                        interactive=False,
+                    )
+
+                with gr.Tab("📈 Point forecast (MASE)"):
+                    gr.Markdown("""Forecast accuracy measured by Mean Absolute Scaled Error.""")
+                    gr.Dataframe(
+                        value=leaderboards["MASE"],
+                        datatype=["markdown", "number", "number", "number"],
+                        interactive=False,
+                    )
+
+        with gr.Tab("📝 About", id=1):
+            gr.Markdown(about.ABOUT_LEADERBOARD)
+
+if __name__ == "__main__":
+    demo.launch(ssr_mode=False)

fev-leaderboard-app.py

@@ -1,9 +0,0 @@
-import streamlit as st
-
-pages = [
-    st.Page("pages/fev_bench.py", title="fev-bench", icon=":material/trophy:"),
-    st.Page("pages/about.py", title="About", icon=":material/info:"),
-]
-
-page = st.navigation(pages)
-page.run()

pages/about.py

@@ -1,19 +0,0 @@
-import streamlit as st
-
-ABOUT_LEADERBOARD = """
-## About
-
-[**fev**](https://github.com/autogluon/fev) is a lightweight wrapper around the 🤗 [datasets](https://huggingface.co/docs/datasets/en/index) library designed to streamline
- benchmarking of time series forecasting models.
-
-### 📚 Resources
-- **Documentation**: [Official docs](https://autogluon.github.io/fev/latest/)
-- **Publication**: ["fev-bench: A Realistic Benchmark for Time Series Forecasting"](https://arxiv.org/abs/2509.26468)
-- **Source Code**: [GitHub repository](https://github.com/autogluon/fev)
-- **Issues & Questions**: [GitHub Issues](https://github.com/autogluon/fev/issues)
-
-### 🚀 Submit Your Model
-Ready to add your model to the leaderboard? Follow this [tutorial](https://autogluon.github.io/fev/latest/tutorials/05-add-your-model/) to evaluate your model with fev and contribute your results.
-"""
-st.set_page_config(layout="wide", page_title="About FEV", page_icon=":material/info:")
-st.markdown(ABOUT_LEADERBOARD)

pages/fev_bench.py

@@ -1,143 +0,0 @@
-import sys
-from pathlib import Path
-
-sys.path.append(str(Path(__file__).parent))
-
-import pandas as pd
-import streamlit as st
-from streamlit.elements.lib.column_types import ColumnConfig
-
-from src.strings import (
-    CITATION_FEV,
-    CITATION_HEADER,
-    FEV_BENCHMARK_BASIC_INFO,
-    FEV_BENCHMARK_DETAILS,
-    PAIRWISE_BENCHMARK_DETAILS,
-    get_pivot_legend,
-)
-from src.utils import (
-    COLORS,
-    construct_pairwise_chart,
-    format_leaderboard,
-    format_metric_name,
-    get_metric_description,
-)
-
-st.set_page_config(layout="wide", page_title="fev leaderboard", page_icon=":material/trophy:")
-
-TITLE = "<h1 style='text-align: center; font-size: 350%;'>fev-bench</h1>"
-SORT_COL = "win_rate"
-AVAILABLE_METRICS = ["SQL", "MASE", "WQL", "WAPE"]
-
-
-@st.cache_data()
-def get_leaderboard(metric_name: str) -> pd.DataFrame:
-    return pd.read_csv(f"tables/leaderboard_{metric_name}.csv")
-
-
-@st.cache_data()
-def get_pairwise(metric_name: str) -> pd.DataFrame:
-    return pd.read_csv(f"tables/pairwise_{metric_name}.csv")
-
-
-@st.cache_data()
-def get_pivot_table(metric_name: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
-    pivot_df = pd.read_csv(f"tables/pivot_{metric_name}.csv")
-    baseline_imputed = pd.read_csv(f"tables/pivot_{metric_name}_baseline_imputed.csv")
-    leakage_imputed = pd.read_csv(f"tables/pivot_{metric_name}_leakage_imputed.csv")
-    return pivot_df, baseline_imputed, leakage_imputed
-
-
-with st.sidebar:
-    selected_metric = st.selectbox("Evaluation Metric", options=AVAILABLE_METRICS, format_func=format_metric_name)
-    st.caption(get_metric_description(selected_metric))
-
-cols = st.columns(spec=[0.025, 0.95, 0.025])
-
-with cols[1] as main_container:
-    st.markdown(TITLE, unsafe_allow_html=True)
-
-    metric_df = get_leaderboard(selected_metric).sort_values(by=SORT_COL, ascending=False)
-    pairwise_df = get_pairwise(selected_metric)
-
-    st.markdown("## :material/trophy: Leaderboard", unsafe_allow_html=True)
-    st.markdown(FEV_BENCHMARK_BASIC_INFO, unsafe_allow_html=True)
-    df_styled = format_leaderboard(metric_df)
-    st.dataframe(
-        df_styled,
-        width="stretch",
-        hide_index=True,
-        column_config={
-            "model_name": ColumnConfig(label="Model Name", alignment="left"),
-            "win_rate": st.column_config.NumberColumn(label="Avg. win rate (%)", format="%.1f"),
-            "skill_score": st.column_config.NumberColumn(label="Skill score (%)", format="%.1f"),
-            "median_inference_time_s": st.column_config.NumberColumn(label="Median runtime (s)", format="%.1f"),
-            "training_corpus_overlap": st.column_config.NumberColumn(label="Leakage (%)", format="%d"),
-            "num_failures": st.column_config.NumberColumn(label="Failed tasks (%)", format="%.0f"),
-            "zero_shot": ColumnConfig(label="Zero-shot", alignment="center"),
-            "org": ColumnConfig(label="Organization", alignment="left"),
-            "link": st.column_config.LinkColumn(label="Link", display_text="🔗"),
-        },
-    )
-
-    with st.expander("See details"):
-        st.markdown(FEV_BENCHMARK_DETAILS, unsafe_allow_html=True)
-
-    st.markdown("## :material/bar_chart: Pairwise comparison", unsafe_allow_html=True)
-    chart_col_1, _, chart_col_2 = st.columns(spec=[0.45, 0.1, 0.45])
-
-    with chart_col_1:
-        st.altair_chart(
-            construct_pairwise_chart(pairwise_df, col="win_rate", metric_name=selected_metric),
-            use_container_width=True,
-        )
-
-    with chart_col_2:
-        st.altair_chart(
-            construct_pairwise_chart(pairwise_df, col="skill_score", metric_name=selected_metric),
-            use_container_width=True,
-        )
-
-    with st.expander("See details"):
-        st.markdown(PAIRWISE_BENCHMARK_DETAILS, unsafe_allow_html=True)
-
-    st.markdown("## :material/table_chart: Results for individual tasks", unsafe_allow_html=True)
-    with st.expander("Show detailed results"):
-        st.markdown(get_pivot_legend("Seasonal Naive", "Chronos-Bolt"), unsafe_allow_html=True)
-        pivot_df, baseline_imputed, leakage_imputed = get_pivot_table(selected_metric)
-        pivot_df = pivot_df.set_index("Task name")
-        baseline_imputed = baseline_imputed.set_index("Task name")
-        leakage_imputed = leakage_imputed.set_index("Task name")
-
-        def style_pivot_table(errors, is_baseline_imputed, is_leakage_imputed):
-            rank_colors = {1: COLORS["gold"], 2: COLORS["silver"], 3: COLORS["bronze"]}
-
-            def highlight_by_position(styler):
-                for row_idx in errors.index:
-                    row_ranks = errors.loc[row_idx].rank(method="min")
-                    for col_idx in errors.columns:
-                        rank = row_ranks[col_idx]
-                        style_parts = []
-                        if rank <= 3:
-                            style_parts.append(f"background-color: {rank_colors[rank]}")
-                        if is_leakage_imputed.loc[row_idx, col_idx]:
-                            style_parts.append(f"color: {COLORS['leakage_impute']}")
-                        elif is_baseline_imputed.loc[row_idx, col_idx]:
-                            style_parts.append(f"color: {COLORS['failure_impute']}")
-                        elif not style_parts:
-                            style_parts.append(f"color: {COLORS['text_default']}")
-                        if style_parts:
-                            styler = styler.map(
-                                lambda x, s="; ".join(style_parts): s,
-                                subset=pd.IndexSlice[row_idx:row_idx, col_idx:col_idx],
-                            )
-                return styler
-
-            return highlight_by_position(errors.style).format(precision=3)
-
-        st.dataframe(style_pivot_table(pivot_df, baseline_imputed, leakage_imputed))
-
-    st.divider()
-    st.markdown("### :material/format_quote: Citation", unsafe_allow_html=True)
-    st.markdown(CITATION_HEADER)
-    st.markdown(CITATION_FEV)

pyproject.toml

@@ -0,0 +1,13 @@
+[tool.ruff]
+# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
+select = ["E", "F"]
+ignore = ["E501"] # line too long (black is taking care of this)
+line-length = 119
+fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
+
+[tool.isort]
+profile = "black"
+line_length = 119
+
+[tool.black]
+line-length = 119

requirements.txt

@@ -1,6 +1,11 @@
+APScheduler
+black
+datasets
+gradio
+gradio[oauth]
+gradio_client
+huggingface-hub>=0.18.0
 matplotlib
 numpy
 pandas
-streamlit==1.49.1
-fev>=0.6.0
-altair>=5.5.0
+fev==0.4.0

save_tables.py

@@ -1,135 +0,0 @@
-#!/usr/bin/env python3
-
-import argparse
-import os
-import sys
-from pathlib import Path
-
-sys.path.append(str(Path(__file__).parent))
-
-import fev
-import pandas as pd
-
-from src.utils import format_leaderboard
-
-# Constants from the main app
-BASELINE_MODEL = "Seasonal Naive"
-LEAKAGE_IMPUTATION_MODEL = "Chronos-Bolt"
-SORT_COL = "win_rate"
-N_RESAMPLES_FOR_CI = 1000
-TOP_K_MODELS_TO_PLOT = 15
-AVAILABLE_METRICS = ["SQL", "MASE", "WQL", "WAPE"]
-
-
-def load_summaries(path="."):
-    csv_files = list(Path(path).glob("*.csv"))
-    if not csv_files:
-        raise FileNotFoundError(f"No CSV files found in {path}")
-    dfs = [pd.read_csv(file) for file in csv_files]
-    return pd.concat(dfs, ignore_index=True)
-
-
-def compute_leaderboard(summaries: pd.DataFrame, metric_name: str) -> pd.DataFrame:
-    lb = fev.analysis.leaderboard(
-        summaries=summaries,
-        metric_column=metric_name,
-        missing_strategy="impute",
-        baseline_model=BASELINE_MODEL,
-        leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
-    )
-    lb = lb.astype("float64").reset_index()
-
-    lb["skill_score"] = lb["skill_score"] * 100
-    lb["win_rate"] = lb["win_rate"] * 100
-    lb["num_failures"] = lb["num_failures"] / summaries["task_name"].nunique() * 100
-    return lb
-
-
-def compute_pairwise(summaries: pd.DataFrame, metric_name: str, included_models: list[str]) -> pd.DataFrame:
-    if BASELINE_MODEL not in included_models:
-        included_models = included_models + [BASELINE_MODEL]
-
-    return (
-        fev.analysis.pairwise_comparison(
-            summaries,
-            included_models=included_models,
-            metric_column=metric_name,
-            baseline_model=BASELINE_MODEL,
-            missing_strategy="impute",
-            n_resamples=N_RESAMPLES_FOR_CI,
-            leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
-        )
-        .round(3)
-        .reset_index()
-    )
-
-
-def compute_pivot_table(summaries: pd.DataFrame, metric_name: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
-    errors = fev.pivot_table(summaries=summaries, metric_column=metric_name, task_columns=["task_name"])
-    train_overlap = (
-        fev.pivot_table(summaries=summaries, metric_column="trained_on_this_dataset", task_columns=["task_name"])
-        .fillna(False)
-        .astype(bool)
-    )
-
-    is_imputed_baseline = errors.isna()
-    is_leakage_imputed = train_overlap
-
-    # Handle imputations
-    errors = errors.mask(train_overlap, errors[LEAKAGE_IMPUTATION_MODEL], axis=0)
-    for col in errors.columns:
-        if col != BASELINE_MODEL:
-            errors[col] = errors[col].fillna(errors[BASELINE_MODEL])
-
-    errors = errors[errors.rank(axis=1).mean().sort_values().index]
-    is_imputed_baseline = is_imputed_baseline[errors.columns]
-    is_leakage_imputed = is_leakage_imputed[errors.columns]
-
-    errors.index.rename("Task name", inplace=True)
-    is_imputed_baseline.index.rename("Task name", inplace=True)
-    is_leakage_imputed.index.rename("Task name", inplace=True)
-
-    return errors.reset_index(), is_imputed_baseline.reset_index(), is_leakage_imputed.reset_index()
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Generate leaderboard tables from CSV summaries")
-    parser.add_argument("-s", "--summaries-path", default=".", help="Path to directory containing CSV files")
-    args = parser.parse_args()
-
-    # Create tables directory
-    tables_dir = Path("tables")
-    tables_dir.mkdir(exist_ok=True)
-
-    print("Loading summaries...")
-    summaries = load_summaries(args.summaries_path)
-
-    for metric in AVAILABLE_METRICS:
-        print(f"Processing {metric}...")
-
-        # Compute leaderboard
-        leaderboard_df = compute_leaderboard(summaries, metric)
-        leaderboard_df.to_csv(tables_dir / f"leaderboard_{metric}.csv", index=False)
-
-        # Get top models for pairwise comparison
-        top_k_models = (
-            leaderboard_df.sort_values(by=SORT_COL, ascending=False).head(TOP_K_MODELS_TO_PLOT)["model_name"].tolist()
-        )
-
-        # Compute pairwise comparison
-        pairwise_df = compute_pairwise(summaries, metric, top_k_models)
-        pairwise_df.to_csv(tables_dir / f"pairwise_{metric}.csv", index=False)
-
-        # Compute pivot table
-        pivot_df, baseline_imputed, leakage_imputed = compute_pivot_table(summaries, metric)
-        pivot_df.to_csv(tables_dir / f"pivot_{metric}.csv", index=False)
-        baseline_imputed.to_csv(tables_dir / f"pivot_{metric}_baseline_imputed.csv", index=False)
-        leakage_imputed.to_csv(tables_dir / f"pivot_{metric}_leakage_imputed.csv", index=False)
-
-        print(f"  Saved: leaderboard_{metric}.csv, pairwise_{metric}.csv, pivot_{metric}.csv")
-
-    print(f"All tables saved to {tables_dir}/")
-
-
-if __name__ == "__main__":
-    main()

src/about.py

@@ -0,0 +1,50 @@
+TITLE = """<h1 align="center" id="space-title">Forecast evaluation leaderboard</h1>"""
+
+# What does your leaderboard evaluate?
+INTRODUCTION_TEXT = """
+This space hosts evaluation results for time series forecasting models.
+
+The results are obtained using [fev](https://github.com/autogluon/fev) - a lightweight library for evaluating time series forecasting models.
+"""
+
+ABOUT_LEADERBOARD = """
+## What is `fev`?
+
+[`fev`](https://github.com/autogluon/fev) is a lightweight wrapper around the 🤗 [`datasets`](https://huggingface.co/docs/datasets/en/index) library that makes it easy to benchmark time series forecasting models.
+
+For more information about `fev`, please check out [github.com/autogluon/fev](https://github.com/autogluon/fev).
+
+Currently, the results in this space are a minimal proof of concept. We plan to add new benchmark datasets and tasks in the future.
+
+## How is `fev` different from other benchmarking tools?
+Existing forecasting benchmarks usually fall into one of two categories:
+
+- Standalone datasets without any supporting infrastructure. These provide no guarantees that the results obtained by different users are comparable. For example, changing the start date or duration of the forecast horizon totally changes the meaning of the scores.
+- Bespoke end-to-end systems that combine models, datasets and forecasting tasks. Such packages usually come with lots of dependencies and assumptions, which makes extending or integrating these libraries into existing systems difficult.
+
+`fev` aims for the middle ground - it provides the core benchmarking functionality without introducing unnecessary constraints or bloated dependencies. The library supports point & probabilistic forecasting, different types of covariates, as well as all popular forecasting metrics.
+
+
+## Submitting your model
+For instructions on how to evaluate your model using `fev` and contribute your results to the leaderboard, please follow the [instructions in the GitHub repo](https://github.com/autogluon/fev/blob/main/docs/04-models.ipynb).
+"""
+
+CHRONOS_BENCHMARK = """
+## Chronos Benchmark II results
+
+This tab contains results for various forecasting models on the 27 datasets used in Benchmark II in the publication [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815).
+
+These datasets were used for zero-shot evaluation of Chronos models (i.e., Chronos models were not trained on these datasets), but some other models did include certain datasets in their training corpus.
+
+Each table contains the following information:
+
+* **Average relative error**: Geometric mean of the relative errors for each task. The relative error for each task is computed as `model_error / baseline_error`.
+* **Average rank**: Arithmetic mean of the ranks achieved by each model on each task.
+* **Median inference time (s)**: Median of the times required to make predictions for the entire dataset (in seconds).
+* **Training corpus overlap (%)**: Percentage of the datasets used in the benchmark that were included in the model's training corpus. Zero-shot models are highlighted in <span style="color:green; font-weight:bold;">green</span>.
+
+Lower values are better for all of the above metrics.
+
+Task definitions and the detailed results are available on [GitHub](https://github.com/autogluon/fev/tree/main/benchmarks/chronos_zeroshot). More information for the datasets is available in [Table 3 of the paper](https://arxiv.org/abs/2403.07815).
+
+"""

src/colors.py

@@ -1,6 +0,0 @@
-# Legacy colors - kept for backward compatibility if needed elsewhere
-VERY_PALE_PURPLE = "#e8d9f3"
-VERY_PALE_GREEN = "#cffdbc"
-VERY_PALE_BLUE = "#d6fffe"
-DEEP_LAVENDER = "#8d5eb7"
-GRASS_GREEN = "#3f9b0b"

src/custom_html_js.py

@@ -0,0 +1,99 @@
+custom_css = """
+
+.markdown-text {
+    font-size: 20px !important;
+}
+
+"""
+
+
+# .tab-buttons button {
+#     font-size: 20px;
+# }
+
+# #citation-button span {
+#     font-size: 16px !important;
+# }
+
+# #citation-button textarea {
+#     font-size: 16px !important;
+# }
+
+# #citation-button > label > button {
+#     margin: 6px;
+#     transform: scale(1.3);
+# }
+
+
+# #leaderboard-table-lite {
+#     margin-top: 15px
+# }
+
+# #search-bar-table-box > div:first-child {
+#     background: none;
+#     border: none;
+# }
+
+# #search-bar {
+#     padding: 0px;
+# }
+
+# /* Hides the final AutoEvalColumn */
+# #llm-benchmark-tab-table table td:last-child,
+# #llm-benchmark-tab-table table th:last-child {
+#     display: none;
+# }
+
+# /* Limit the width of the first AutoEvalColumn so that names don't expand too much */
+# table td:first-child,
+# table th:first-child {
+#     max-width: 400px;
+#     overflow: auto;
+#     white-space: nowrap;
+# }
+
+
+# #scale-logo {
+#     border-style: none !important;
+#     box-shadow: none;
+#     display: block;
+#     margin-left: auto;
+#     margin-right: auto;
+#     max-width: 600px;
+# }
+
+# #scale-logo .download {
+#     display: none;
+# }
+# #filter_type{
+#     border: 0;
+#     padding-left: 0;
+#     padding-top: 0;
+# }
+# #filter_type label {
+#     display: flex;
+# }
+# #filter_type label > span{
+#     margin-top: var(--spacing-lg);
+#     margin-right: 0.5em;
+# }
+# #filter_type label > .wrap{
+#     width: 103px;
+# }
+# #filter_type label > .wrap .wrap-inner{
+#     padding: 2px;
+# }
+# #filter_type label > .wrap .wrap-inner input{
+#     width: 1px
+# }
+# #filter-columns-type{
+#     border:0;
+#     padding:0.5;
+# }
+# #filter-columns-size{
+#     border:0;
+#     padding:0.5;
+# }
+# #box-filter > .form{
+#     border: 0
+# }

src/formatting.py

@@ -0,0 +1,31 @@
+def model_hyperlink(link, model_name):
+    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+
+
+MODEL_URLS = {
+    "chronos_tiny": "amazon/chronos-t5-tiny",
+    "chronos_mini": "amazon/chronos-t5-mini",
+    "chronos_small": "amazon/chronos-t5-small",
+    "chronos_base": "amazon/chronos-t5-base",
+    "chronos_large": "amazon/chronos-t5-large",
+    "chronos_bolt_tiny": "amazon/chronos-bolt-tiny",
+    "chronos_bolt_mini": "amazon/chronos-bolt-mini",
+    "chronos_bolt_small": "amazon/chronos-bolt-small",
+    "chronos_bolt_base": "amazon/chronos-bolt-base",
+    "moirai_large": "Salesforce/moirai-1.1-R-large",
+    "moirai_base": "Salesforce/moirai-1.1-R-base",
+    "moirai_small": "Salesforce/moirai-1.1-R-small",
+    "timesfm": "google/timesfm-1.0-200m-pytorch",
+    "timesfm-2.0": "google/timesfm-2.0-500m-pytorch",
+    "ttm-r2": "ibm-granite/granite-timeseries-ttm-r2",
+    "tirex": "NX-AI/TiRex",
+}
+
+
+def make_clickable_model(model_name):
+    if model_name in MODEL_URLS:
+        model_path = MODEL_URLS.get(model_name)
+        link = f"https://huggingface.co/{model_path}"
+        return model_hyperlink(link, model_name)
+    else:
+        return model_name

src/streamlit_app.py

@@ -1,9 +0,0 @@
-import streamlit as st
-
-pages = [
-    st.Page("../pages/fev_bench.py", title="fev-bench", icon=":material/trophy:"),
-    st.Page("../pages/about.py", title="About", icon=":material/info:"),
-]
-
-page = st.navigation(pages)
-page.run()

src/strings.py

@@ -1,114 +0,0 @@
-from src.utils import COLORS
-
-INTRODUCTION_TEXT = """
-This space hosts evaluation results for time series forecasting models. The results are obtained using [fev](https://github.com/autogluon/fev) - a lightweight library for evaluating time series forecasting models.
-"""
-
-LEGEND = """
-"""
-
-TABLE_INFO = f"""
-The leaderboard summarizes the performance of all models evaluated on the 100 tasks comprising **fev-bench**. More details available in the [paper](https://arxiv.org/abs/2509.26468).
-
-Model names are colored by type: <span style='color: {COLORS["dl_text"]}; font-weight: bold;'>Deep Learning</span> and <span style='color: {COLORS["st_text"]}; font-weight: bold;'>Statistical</span>.
-
-The full matrix $E_{{rj}}$ with the error of each model $j$ on task $r$ is available at the bottom of the page.
-
-* **Avg. win rate (%)**: Fraction of all possible model pairs and tasks where this model achieves lower error than the competing model. For model $j$, defined as $W_j = \\frac{{1}}{{R(M-1)}} \\sum_{{r=1}}^{{R}} \\sum_{{k \\neq j}} (\\mathbf{{1}}(E_{{rj}} < E_{{rk}}) + 0.5 \\cdot \\mathbf{{1}}(E_{{rj}} = E_{{rk}}))$ where $R$ is number of tasks, $M$ is number of models. Ties count as half-wins.
-
-    Ranges from 0% (worst) to 100% (best). Higher values are better. This value changes as new models are added to the benchmark.
-
-* **Skill score (%)**: Measures how much the model reduces forecasting error compared to the Seasonal Naive baseline. Computed as $S_j = 100 \\times (1 - \\sqrt[R]{{\\prod_{{r=1}}^{{R}} E_{{rj}}/E_{{r\\beta}}}})$, where $E_{{r\\beta}}$ is baseline error on task $r$. Relative errors are clipped between 0.01 and 100 before aggregation to avoid extreme outliers. Positive values indicate better-than-baseline performance, negative values indicate worse-than-baseline performance.
-
-    Higher values are better. This value does not change as new models are added to the benchmark.
-
-* **Median runtime (s)**: Median end-to-end time (training + prediction across all evaluation windows) in seconds. Note that inference times depend on hardware, batch sizes, and implementation details, so these serve as a rough guide rather than definitive performance benchmarks.
-
-* **Leakage (%)**: For zero-shot models, percentage of benchmark datasets included in the model's training corpus. Results for tasks with reported overlap are replaced with Chronos-Bolt (Base) performance to prevent data leakage.
-
-* **Failed tasks (%)**: Percentage of tasks where the model failed to produce a forecast. Results for failed tasks are replaced with Seasonal Naive performance.
-
-* **Zero-shot**: Indicates whether the model can make predictions without task-specific training (✓ = zero-shot, × = task-specific).
-"""
-
-CHRONOS_BENCHMARK_BASIC_INFO = f"""
-**Chronos Benchmark II** contains results for various forecasting models on the 27 datasets used in Benchmark II in the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). {LEGEND}
-"""
-
-CHRONOS_BENCHMARK_DETAILS = f"""
-{TABLE_INFO}
-
-Task definitions and the detailed results are available on [GitHub](https://github.com/autogluon/fev/tree/main/benchmarks/chronos_zeroshot). More information for the datasets is available in [Table 3 of the paper](https://arxiv.org/abs/2403.07815).
-"""
-
-FEV_BENCHMARK_BASIC_INFO = f"""
-Results for various forecasting models on 100 tasks of the **fev-bench** benchmark, as described in the paper [fev-bench: A Realistic Benchmark for Time Series Forecasting](https://arxiv.org/abs/2509.26468). {LEGEND}
-"""
-
-FEV_BENCHMARK_DETAILS = f"""
-{TABLE_INFO}
-
-Task definitions and the detailed results are available on [GitHub](https://github.com/autogluon/fev/tree/main/benchmarks/). Datasets used for evaluation are available on [Hugging Face](https://huggingface.co/datasets/autogluon/fev_datasets).
-"""
-
-CITATION_HEADER = """
-If you find this leaderboard useful for your research, please consider citing the associated paper(s):
-
-"""
-CITATION_FEV = """
-```
-@article{shchur2025fev,
-  title={{fev-bench}: A Realistic Benchmark for Time Series Forecasting},
-  author={Shchur, Oleksandr and Ansari, Abdul Fatir and Turkmen, Caner and Stella, Lorenzo and Erickson, Nick and Guerron, Pablo and Bohlke-Schneider, Michael and Wang, Yuyang},
-  year={2025},
-  eprint={2509.26468},
-  archivePrefix={arXiv},
-  primaryClass={cs.LG}
-}
-```
-"""
-
-
-def get_pivot_legend(baseline_model: str, leakage_imputation_model: str) -> str:
-    return f"""
-Task definitions and raw results in CSV format are available on [GitHub](https://github.com/autogluon/fev/tree/main/benchmarks/fev_bench).
-
-Best results for each task are marked with
-<span style='background: {COLORS["gold"]}; color: {COLORS["text_default"]}; padding: 3px; border-radius: 5px;'>🥇 1st</span>
-<span style='background: {COLORS["silver"]}; color: {COLORS["text_default"]}; padding: 3px; border-radius: 5px;'>🥈 2nd</span>
-<span style='background: {COLORS["bronze"]}; color: {COLORS["text_default"]}; padding: 3px; border-radius: 5px;'>🥉 3rd</span>
-<br><br>
-**Imputation:**
-- <span style='color: {COLORS["failure_impute"]}; font-weight: bold;'>Failed tasks</span> imputed by {baseline_model}
-- <span style='color: {COLORS["leakage_impute"]}; font-weight: bold;'>Leaky tasks</span> imputed by {leakage_imputation_model}
-"""
-
-
-PAIRWISE_BENCHMARK_DETAILS = """
-The pairwise charts show head-to-head results between models:
-
-* **Win rate**: Percentage of tasks where Model 1 achieves lower error than Model 2 (ties count as half-wins).
-  A value above 50% means Model 1 is more accurate than Model 2 on average.
-
-* **Skill score**: Average relative error reduction of Model 1 with respect to Model 2.
-  A positive value means Model 1 reduces forecasting error compared to Model 2 on average.
-
-**Confidence Intervals**: 95% intervals are estimated using 1000 bootstrap samples over tasks.
-For each bootstrap sample, tasks are resampled with replacement and the pairwise win rate / skill score are recomputed.
-The intervals correspond to the 2.5th and 97.5th percentiles of these bootstrap distributions,
-capturing how model comparisons vary under alternative benchmark compositions.
-"""
-
-
-CITATION_CHRONOS = """
-```
-@article{ansari2024chronos,
-  title={Chronos: Learning the Language of Time Series},
-  author={Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Wang, Hao and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang},
-  journal={Transactions on Machine Learning Research},
-  issn={2835-8856},
-  year={2024},
-  url={https://openreview.net/forum?id=gerNCVqqtR}
-}
-```
-"""

src/utils.py

@@ -1,374 +0,0 @@
-import altair as alt
-import fev
-import pandas as pd
-import pandas.io.formats.style
-
-# Color constants - all colors defined in one place
-
-COLORS = {
-    "dl_text": "#5A7FA5",
-    "st_text": "#A5795A",
-    # "st_text": "#666666",
-    "bar_fill": "#8d5eb7",
-    "error_bar": "#222222",
-    "point": "#111111",
-    "text_white": "white",
-    "text_black": "black",
-    "text_default": "#111",
-    "gold": "#F7D36B",
-    "silver": "#E5E7EB",
-    "bronze": "#E6B089",
-    "leakage_impute": "#3B82A0",
-    "failure_impute": "#E07B39",
-}
-HEATMAP_COLOR_SCHEME = "purplegreen"
-
-# Model configuration: (url, org, zero_shot, model_type)
-MODEL_CONFIG = {
-    # Chronos Models
-    "chronos_tiny": ("amazon/chronos-t5-tiny", "AWS", True, "DL"),
-    "chronos_mini": ("amazon/chronos-t5-mini", "AWS", True, "DL"),
-    "chronos_small": ("amazon/chronos-t5-small", "AWS", True, "DL"),
-    "chronos_base": ("amazon/chronos-t5-base", "AWS", True, "DL"),
-    "chronos_large": ("amazon/chronos-t5-large", "AWS", True, "DL"),
-    "chronos_bolt_tiny": ("amazon/chronos-bolt-tiny", "AWS", True, "DL"),
-    "chronos_bolt_mini": ("amazon/chronos-bolt-mini", "AWS", True, "DL"),
-    "chronos_bolt_small": ("amazon/chronos-bolt-small", "AWS", True, "DL"),
-    "chronos_bolt_base": ("amazon/chronos-bolt-base", "AWS", True, "DL"),
-    "chronos-bolt": ("amazon/chronos-bolt-base", "AWS", True, "DL"),
-    "chronos-2": ("amazon/chronos-2", "AWS", True, "DL"),
-    # Moirai Models
-    "moirai_large": ("Salesforce/moirai-1.1-R-large", "Salesforce", True, "DL"),
-    "moirai_base": ("Salesforce/moirai-1.1-R-base", "Salesforce", True, "DL"),
-    "moirai_small": ("Salesforce/moirai-1.1-R-small", "Salesforce", True, "DL"),
-    "moirai-2.0": ("Salesforce/moirai-2.0-R-small", "Salesforce", True, "DL"),
-    # TimesFM Models
-    "timesfm": ("google/timesfm-1.0-200m-pytorch", "Google", True, "DL"),
-    "timesfm-2.0": ("google/timesfm-2.0-500m-pytorch", "Google", True, "DL"),
-    "timesfm-2.5": ("google/timesfm-2.5-200m-pytorch", "Google", True, "DL"),
-    # Toto Models
-    "toto-1.0": ("Datadog/Toto-Open-Base-1.0", "Datadog", True, "DL"),
-    # Other Models
-    "tirex": ("NX-AI/TiRex", "NX-AI", True, "DL"),
-    "tabpfn-ts": ("Prior-Labs/TabPFN-v2-reg", "Prior Labs", True, "DL"),
-    "sundial-base": ("thuml/sundial-base-128m", "Tsinghua University", True, "DL"),
-    "ttm-r2": ("ibm-granite/granite-timeseries-ttm-r2", "IBM", True, "DL"),
-    # Task-specific models
-    "stat. ensemble": (
-        "https://nixtlaverse.nixtla.io/statsforecast/",
-        "—",
-        False,
-        "ST",
-    ),
-    "autoarima": ("https://nixtlaverse.nixtla.io/statsforecast/", "—", False, "ST"),
-    "autotheta": ("https://nixtlaverse.nixtla.io/statsforecast/", "—", False, "ST"),
-    "autoets": ("https://nixtlaverse.nixtla.io/statsforecast/", "—", False, "ST"),
-    "seasonalnaive": ("https://nixtlaverse.nixtla.io/statsforecast/", "—", False, "ST"),
-    "seasonal naive": (
-        "https://nixtlaverse.nixtla.io/statsforecast/",
-        "—",
-        False,
-        "ST",
-    ),
-    "drift": ("https://nixtlaverse.nixtla.io/statsforecast/", "—", False, "ST"),
-    "naive": ("https://nixtlaverse.nixtla.io/statsforecast/", "—", False, "ST"),
-}
-
-
-ALL_METRICS = {
-    "SQL": (
-        "SQL: Scaled Quantile Loss",
-        "The [Scaled Quantile Loss (SQL)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.SQL) is a **scale-invariant** metric for evaluating **probabilistic** forecasts.",
-    ),
-    "MASE": (
-        "MASE: Mean Absolute Scaled Error",
-        "The [Mean Absolute Scaled Error (MASE)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.MASE) is a **scale-invariant** metric for evaluating **point** forecasts.",
-    ),
-    "WQL": (
-        "WQL: Weighted Quantile Loss",
-        "The [Weighted Quantile Loss (WQL)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.WQL), is a **scale-dependent** metric for evaluating **probabilistic** forecasts.",
-    ),
-    "WAPE": (
-        "WAPE: Weighted Absolute Percentage Error",
-        "The [Weighted Absolute Percentage Error (WAPE)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.WAPE) is a **scale-dependent** metric for evaluating **point** forecasts.",
-    ),
-}
-
-
-def format_metric_name(metric_name: str):
-    return ALL_METRICS[metric_name][0]
-
-
-def get_metric_description(metric_name: str):
-    return ALL_METRICS[metric_name][1]
-
-
-def get_model_link(model_name):
-    config = MODEL_CONFIG.get(model_name.lower())
-    if not config or not config[0]:
-        return ""
-    url = config[0]
-    return url if url.startswith("https:") else f"https://huggingface.co/{url}"
-
-
-def get_model_organization(model_name):
-    config = MODEL_CONFIG.get(model_name.lower())
-    return config[1] if config else "—"
-
-
-def get_zero_shot_status(model_name):
-    config = MODEL_CONFIG.get(model_name.lower())
-    return "✓" if config and config[2] else "×"
-
-
-def get_model_type(model_name):
-    config = MODEL_CONFIG.get(model_name.lower())
-    return config[3] if config else "—"
-
-
-def highlight_model_type_color(cell):
-    config = MODEL_CONFIG.get(cell.lower())
-    if config:
-        color = COLORS["dl_text"] if config[3] == "DL" else COLORS["st_text"]
-        return f"font-weight: bold; color: {color}"
-    return "font-weight: bold"
-
-
-def format_leaderboard(df: pd.DataFrame):
-    df = df.copy()
-    df["skill_score"] = df["skill_score"].round(1)
-    df["win_rate"] = df["win_rate"].round(1)
-    df["zero_shot"] = df["model_name"].apply(get_zero_shot_status)
-    # Format leakage column: convert to int for all models, 0 for non-zero-shot
-    df["training_corpus_overlap"] = df.apply(
-        lambda row: int(round(row["training_corpus_overlap"] * 100)) if row["zero_shot"] == "✓" else 0,
-        axis=1,
-    )
-    df["link"] = df["model_name"].apply(get_model_link)
-    df["org"] = df["model_name"].apply(get_model_organization)
-    df = df[
-        [
-            "model_name",
-            "win_rate",
-            "skill_score",
-            "median_inference_time_s",
-            "training_corpus_overlap",
-            "num_failures",
-            "zero_shot",
-            "org",
-            "link",
-        ]
-    ]
-    return (
-        df.style.map(highlight_model_type_color, subset=["model_name"])
-        .map(lambda x: "font-weight: bold", subset=["zero_shot"])
-        .apply(
-            lambda x: ["background-color: #f8f9fa" if i % 2 == 1 else "" for i in range(len(x))],
-            axis=0,
-        )
-    )
-
-
-def construct_bar_chart(df: pd.DataFrame, col: str, metric_name: str):
-    label = "Skill Score" if col == "skill_score" else "Win Rate"
-
-    tooltip = [
-        alt.Tooltip("model_name:N"),
-        alt.Tooltip(f"{col}:Q", format=".2f"),
-        alt.Tooltip(f"{col}_lower:Q", title="95% CI Lower", format=".2f"),
-        alt.Tooltip(f"{col}_upper:Q", title="95% CI Upper", format=".2f"),
-    ]
-
-    base_encode = {
-        "y": alt.Y("model_name:N", title="Forecasting Model", sort=None),
-        "tooltip": tooltip,
-    }
-
-    bars = (
-        alt.Chart(df)
-        .mark_bar(color=COLORS["bar_fill"], cornerRadius=4)
-        .encode(
-            x=alt.X(f"{col}:Q", title=f"{label} (%)", scale=alt.Scale(zero=False)),
-            **base_encode,
-        )
-    )
-
-    error_bars = (
-        alt.Chart(df)
-        .mark_errorbar(ticks={"height": 5}, color=COLORS["error_bar"])
-        .encode(
-            y=alt.Y("model_name:N", title=None, sort=None),
-            x=alt.X(f"{col}_lower:Q", title=f"{label} (%)"),
-            x2=alt.X2(f"{col}_upper:Q"),
-            tooltip=tooltip,
-        )
-    )
-
-    points = (
-        alt.Chart(df)
-        .mark_point(filled=True, color=COLORS["point"])
-        .encode(x=alt.X(f"{col}:Q", title=f"{label} (%)"), **base_encode)
-    )
-
-    return (
-        (bars + error_bars + points)
-        .properties(height=500, title=f"{label} ({metric_name}) with 95% CIs")
-        .configure_title(fontSize=16)
-    )
-
-
-def construct_pairwise_chart(df: pd.DataFrame, col: str, metric_name: str):
-    config = {
-        "win_rate": ("Win Rate", [0, 100], 50, f"abs(datum.{col} - 50) > 30"),
-        "skill_score": ("Skill Score", [-15, 15], 0, f"abs(datum.{col}) > 10"),
-    }
-    cbar_label, domain, domain_mid, text_condition = config[col]
-
-    df = df.copy()
-    for c in [col, f"{col}_lower", f"{col}_upper"]:
-        df[c] *= 100
-
-    model_order = df.groupby("model_1")[col].mean().sort_values(ascending=False).index.tolist()
-
-    tooltip = [
-        alt.Tooltip("model_1:N", title="Model 1"),
-        alt.Tooltip("model_2:N", title="Model 2"),
-        alt.Tooltip(f"{col}:Q", title=cbar_label.split(" ")[0], format=".1f"),
-        alt.Tooltip(f"{col}_lower:Q", title="95% CI Lower", format=".1f"),
-        alt.Tooltip(f"{col}_upper:Q", title="95% CI Upper", format=".1f"),
-    ]
-
-    base = alt.Chart(df).encode(
-        x=alt.X(
-            "model_2:N",
-            sort=model_order,
-            title="Model 2",
-            axis=alt.Axis(orient="top", labelAngle=-90),
-        ),
-        y=alt.Y("model_1:N", sort=model_order, title="Model 1"),
-    )
-
-    heatmap = base.mark_rect().encode(
-        color=alt.Color(
-            f"{col}:Q",
-            legend=None,
-            scale=alt.Scale(
-                scheme=HEATMAP_COLOR_SCHEME,
-                domain=domain,
-                domainMid=domain_mid,
-                clamp=True,
-            ),
-        ),
-        tooltip=tooltip,
-    )
-
-    text_main = base.mark_text(dy=-8, fontSize=8, baseline="top", yOffset=5).encode(
-        text=alt.Text(f"{col}:Q", format=".1f"),
-        color=alt.condition(
-            text_condition,
-            alt.value(COLORS["text_white"]),
-            alt.value(COLORS["text_black"]),
-        ),
-        tooltip=tooltip,
-    )
-
-    return (
-        (heatmap + text_main)
-        .properties(
-            height=550,
-            title={
-                "text": f"Pairwise {cbar_label} ({metric_name}) with 95% CIs",
-                "fontSize": 16,
-            },
-        )
-        .configure_axis(labelFontSize=11, titleFontSize=13, titleFontWeight="bold")
-        .resolve_scale(color="independent")
-    )
-
-
-def construct_pivot_table_from_df(errors: pd.DataFrame, metric_name: str) -> pd.io.formats.style.Styler:
-    """Construct styled pivot table from precomputed DataFrame."""
-
-    def highlight_by_position(styler):
-        rank_colors = {1: COLORS["gold"], 2: COLORS["silver"], 3: COLORS["bronze"]}
-
-        for row_idx in errors.index:
-            row_ranks = errors.loc[row_idx].rank(method="min")
-            for col_idx in errors.columns:
-                rank = row_ranks[col_idx]
-                style_parts = []
-
-                # Rank background colors
-                if rank <= 3:
-                    style_parts.append(f"background-color: {rank_colors[rank]}")
-                else:
-                    style_parts.append(f"color: {COLORS['text_default']}")
-
-                if style_parts:
-                    styler = styler.map(
-                        lambda x, s="; ".join(style_parts): s,
-                        subset=pd.IndexSlice[row_idx:row_idx, col_idx:col_idx],
-                    )
-        return styler
-
-    return highlight_by_position(errors.style).format(precision=3)
-
-
-def construct_pivot_table(
-    summaries: pd.DataFrame,
-    metric_name: str,
-    baseline_model: str,
-    leakage_imputation_model: str,
-) -> pd.io.formats.style.Styler:
-    errors = fev.pivot_table(summaries=summaries, metric_column=metric_name, task_columns=["task_name"])
-    train_overlap = (
-        fev.pivot_table(
-            summaries=summaries,
-            metric_column="trained_on_this_dataset",
-            task_columns=["task_name"],
-        )
-        .fillna(False)
-        .astype(bool)
-    )
-
-    is_imputed_baseline = errors.isna()
-    is_leakage_imputed = train_overlap
-
-    # Handle imputations
-    errors = errors.mask(train_overlap, errors[leakage_imputation_model], axis=0)
-    for col in errors.columns:
-        if col != baseline_model:
-            errors[col] = errors[col].fillna(errors[baseline_model])
-
-    errors = errors[errors.rank(axis=1).mean().sort_values().index]
-    errors.index.rename("Task name", inplace=True)
-
-    def highlight_by_position(styler):
-        rank_colors = {1: COLORS["gold"], 2: COLORS["silver"], 3: COLORS["bronze"]}
-
-        for row_idx in errors.index:
-            row_ranks = errors.loc[row_idx].rank(method="min")
-            for col_idx in errors.columns:
-                rank = row_ranks[col_idx]
-                style_parts = []
-
-                # Rank background colors
-                if rank <= 3:
-                    style_parts.append(f"background-color: {rank_colors[rank]}")
-
-                # Imputation text colors
-                if is_leakage_imputed.loc[row_idx, col_idx]:
-                    style_parts.append(f"color: {COLORS['leakage_impute']}")
-                elif is_imputed_baseline.loc[row_idx, col_idx]:
-                    style_parts.append(f"color: {COLORS['failure_impute']}")
-                elif not style_parts or (len(style_parts) == 1 and "font-weight" in style_parts[0]):
-                    style_parts.append(f"color: {COLORS['text_default']}")
-
-                if style_parts:
-                    styler = styler.map(
-                        lambda x, s="; ".join(style_parts): s,
-                        subset=pd.IndexSlice[row_idx:row_idx, col_idx:col_idx],
-                    )
-        return styler
-
-    return highlight_by_position(errors.style).format(precision=3)

tables/leaderboard_MASE.csv

@@ -1,16 +0,0 @@
-model_name,win_rate,skill_score,median_training_time_s,median_inference_time_s,training_corpus_overlap,num_failures
-Chronos-2,88.07142857142857,35.495567419618524,0.0,3.56996066,0.0,0.0
-TiRex,76.89285714285712,30.012261671412087,0.0,1.4030189444999999,0.01,0.0
-TimesFM-2.5,75.07142857142857,30.197590695974842,0.0,16.9308283315,0.1,0.0
-Toto-1.0,66.85714285714285,28.213885409107164,0.0,90.676829282,0.08,0.0
-Moirai-2.0,61.21428571428572,27.25905867510979,0.0,2.5351729785000003,0.28,0.0
-Chronos-Bolt,60.785714285714285,26.51514785224567,0.0,0.9960156920000001,0.0,0.0
-TabPFN-TS,58.67857142857142,27.649834806479856,0.0,305.466367349,0.0,2.0
-Sundial-Base,53.39285714285714,24.746214191232585,0.0,35.620029862500004,0.01,0.0
-Stat. Ensemble,48.535714285714285,15.654207763455553,0.0,690.615290623,0.0,11.0
-AutoARIMA,36.67857142857143,11.239602667684679,0.0,186.7699845295,0.0,10.0
-AutoTheta,34.92857142857142,10.9884701559679,0.0,9.267665384499999,0.0,0.0
-AutoETS,33.25,2.258981670108584,0.0,17.004582018,0.0,3.0
-Seasonal Naive,20.96428571428571,0.0,0.0,2.3247850175,0.0,0.0
-Naive,19.321428571428573,-16.673948793089565,0.0,2.2371214229999996,0.0,0.0
-Drift,15.357142857142856,-18.13920384837251,0.0,2.1929671395000003,0.0,0.0

tables/leaderboard_SQL.csv

@@ -1,16 +0,0 @@
-model_name,win_rate,skill_score,median_training_time_s,median_inference_time_s,training_corpus_overlap,num_failures
-Chronos-2,91.42857142857143,47.27916337543932,0.0,3.56996066,0.0,0.0
-TiRex,82.67857142857142,42.57610775202675,0.0,1.4030189444999999,0.01,0.0
-TimesFM-2.5,77.57142857142858,42.20062128325953,0.0,16.9308283315,0.1,0.0
-Toto-1.0,70.21428571428571,40.7460359139889,0.0,90.676829282,0.08,0.0
-Chronos-Bolt,64.42857142857143,38.892961600022936,0.0,0.9960156920000001,0.0,0.0
-Moirai-2.0,64.42857142857143,39.332013785815214,0.0,2.5351729785000003,0.28,0.0
-TabPFN-TS,62.96428571428573,39.58671179038912,0.0,305.466367349,0.0,2.0
-Sundial-Base,45.964285714285715,33.42287717226134,0.0,35.620029862500004,0.01,0.0
-Stat. Ensemble,45.53571428571429,20.161731427800046,0.0,690.615290623,0.0,11.0
-AutoARIMA,40.67857142857143,20.561948549632326,0.0,186.7699845295,0.0,10.0
-AutoETS,33.67857142857143,-26.818526760288375,0.0,17.004582018,0.0,3.0
-AutoTheta,27.142857142857142,5.457380397818312,0.0,9.267665384499999,0.0,0.0
-Seasonal Naive,20.178571428571423,0.0,0.0,2.3247850175,0.0,0.0
-Naive,13.821428571428573,-45.398988807164976,0.0,2.2371214229999996,0.0,0.0
-Drift,9.285714285714286,-45.77585379444895,0.0,2.1929671395000003,0.0,0.0

tables/leaderboard_WAPE.csv

@@ -1,16 +0,0 @@
-model_name,win_rate,skill_score,median_training_time_s,median_inference_time_s,training_corpus_overlap,num_failures
-Chronos-2,85.92857142857142,39.409528419872345,0.0,3.56996066,0.0,0.0
-TimesFM-2.5,75.28571428571428,33.719263075740194,0.0,16.9308283315,0.1,0.0
-TiRex,75.25000000000001,33.56017122067458,0.0,1.4030189444999999,0.01,0.0
-Toto-1.0,67.64285714285717,31.488756179777678,0.0,90.676829282,0.08,0.0
-TabPFN-TS,64.32142857142857,33.36123166513091,0.0,305.466367349,0.0,2.0
-Moirai-2.0,62.14285714285715,30.651048841637785,0.0,2.5351729785000003,0.28,0.0
-Chronos-Bolt,61.142857142857146,29.770064235526473,0.0,0.9960156920000001,0.0,0.0
-Sundial-Base,51.24999999999999,27.263435775595426,0.0,35.620029862500004,0.01,0.0
-Stat. Ensemble,47.32142857142856,17.693885547849597,0.0,690.615290623,0.0,11.0
-AutoARIMA,34.96428571428571,13.271687847205381,0.0,186.7699845295,0.0,10.0
-AutoETS,34.60714285714286,4.328689341465408,0.0,17.004582018,0.0,3.0
-AutoTheta,32.0,13.794755020224814,0.0,9.267665384499999,0.0,0.0
-Naive,22.749999999999996,-6.11148881462471,0.0,2.2371214229999996,0.0,0.0
-Seasonal Naive,19.249999999999996,0.0,0.0,2.3247850175,0.0,0.0
-Drift,16.142857142857146,-8.604851625817744,0.0,2.1929671395000003,0.0,0.0

tables/leaderboard_WQL.csv

@@ -1,16 +0,0 @@
-model_name,win_rate,skill_score,median_training_time_s,median_inference_time_s,training_corpus_overlap,num_failures
-Chronos-2,89.14285714285714,51.51837694099346,0.0,3.56996066,0.0,0.0
-TiRex,80.60714285714286,46.66550397812589,0.0,1.4030189444999999,0.01,0.0
-TimesFM-2.5,78.28571428571429,46.72558173490076,0.0,16.9308283315,0.1,0.0
-Toto-1.0,70.85714285714285,45.00088243993733,0.0,90.676829282,0.08,0.0
-TabPFN-TS,68.10714285714286,45.822378843833036,0.0,305.466367349,0.0,2.0
-Moirai-2.0,66.21428571428571,43.864523387242194,0.0,2.5351729785000003,0.28,0.0
-Chronos-Bolt,64.85714285714286,43.187343947848866,0.0,0.9960156920000001,0.0,0.0
-Sundial-Base,47.035714285714285,37.43731640370259,0.0,35.620029862500004,0.01,0.0
-Stat. Ensemble,44.17857142857143,21.795752415252334,0.0,690.615290623,0.0,11.0
-AutoARIMA,40.10714285714287,23.401617100945593,0.0,186.7699845295,0.0,10.0
-AutoETS,31.392857142857146,-27.026777935471568,0.0,17.004582018,0.0,3.0
-AutoTheta,26.714285714285708,7.846425960422055,0.0,9.267665384499999,0.0,0.0
-Seasonal Naive,19.464285714285708,0.0,0.0,2.3247850175,0.0,0.0
-Naive,13.750000000000002,-39.121433468308894,0.0,2.2371214229999996,0.0,0.0
-Drift,9.285714285714288,-40.05851008470427,0.0,2.1929671395000003,0.0,0.0

tables/pairwise_MASE.csv

@@ -1,226 +0,0 @@
-model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
-Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
-Chronos-2,TiRex,0.72,0.63,0.81,0.078,0.052,0.108
-Chronos-2,TimesFM-2.5,0.71,0.62,0.79,0.076,0.048,0.103
-Chronos-2,Toto-1.0,0.74,0.65,0.83,0.101,0.073,0.133
-Chronos-2,Moirai-2.0,0.92,0.86,0.97,0.113,0.086,0.141
-Chronos-2,Chronos-Bolt,0.94,0.89,0.98,0.122,0.096,0.15
-Chronos-2,TabPFN-TS,0.85,0.77,0.92,0.108,0.08,0.14
-Chronos-2,Sundial-Base,0.91,0.85,0.96,0.143,0.113,0.172
-Chronos-2,Stat. Ensemble,0.86,0.79,0.93,0.235,0.19,0.277
-Chronos-2,AutoARIMA,0.95,0.9,0.99,0.273,0.23,0.315
-Chronos-2,AutoTheta,0.94,0.89,0.98,0.275,0.236,0.316
-Chronos-2,AutoETS,0.89,0.83,0.95,0.34,0.28,0.401
-Chronos-2,Seasonal Naive,0.98,0.95,1.0,0.355,0.318,0.393
-Chronos-2,Naive,0.98,0.95,1.0,0.447,0.388,0.505
-Chronos-2,Drift,0.94,0.89,0.98,0.454,0.392,0.513
-TiRex,Chronos-2,0.28,0.19,0.37,-0.085,-0.121,-0.055
-TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
-TiRex,TimesFM-2.5,0.485,0.385,0.585,-0.003,-0.019,0.014
-TiRex,Toto-1.0,0.615,0.52,0.71,0.025,0.003,0.046
-TiRex,Moirai-2.0,0.765,0.685,0.845,0.038,0.021,0.055
-TiRex,Chronos-Bolt,0.765,0.68,0.845,0.048,0.029,0.067
-TiRex,TabPFN-TS,0.69,0.6,0.78,0.033,-0.01,0.074
-TiRex,Sundial-Base,0.805,0.72,0.88,0.07,0.036,0.104
-TiRex,Stat. Ensemble,0.81,0.73,0.89,0.17,0.132,0.21
-TiRex,AutoARIMA,0.9,0.84,0.95,0.211,0.175,0.253
-TiRex,AutoTheta,0.94,0.89,0.98,0.214,0.18,0.249
-TiRex,AutoETS,0.84,0.77,0.91,0.284,0.225,0.345
-TiRex,Seasonal Naive,0.97,0.94,1.0,0.3,0.264,0.339
-TiRex,Naive,0.96,0.92,0.99,0.4,0.344,0.457
-TiRex,Drift,0.94,0.89,0.98,0.408,0.347,0.462
-TimesFM-2.5,Chronos-2,0.29,0.21,0.38,-0.082,-0.115,-0.05
-TimesFM-2.5,TiRex,0.515,0.415,0.615,0.003,-0.014,0.019
-TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
-TimesFM-2.5,Toto-1.0,0.57,0.485,0.66,0.028,0.005,0.051
-TimesFM-2.5,Moirai-2.0,0.71,0.625,0.795,0.04,0.021,0.06
-TimesFM-2.5,Chronos-Bolt,0.71,0.625,0.79,0.05,0.029,0.071
-TimesFM-2.5,TabPFN-TS,0.67,0.58,0.77,0.035,-0.011,0.081
-TimesFM-2.5,Sundial-Base,0.805,0.73,0.88,0.072,0.04,0.105
-TimesFM-2.5,Stat. Ensemble,0.79,0.71,0.87,0.172,0.126,0.217
-TimesFM-2.5,AutoARIMA,0.87,0.8,0.93,0.214,0.175,0.257
-TimesFM-2.5,AutoTheta,0.9,0.83,0.95,0.216,0.177,0.255
-TimesFM-2.5,AutoETS,0.84,0.76,0.91,0.286,0.228,0.347
-TimesFM-2.5,Seasonal Naive,0.96,0.92,0.99,0.302,0.267,0.342
-TimesFM-2.5,Naive,0.96,0.92,0.99,0.402,0.341,0.458
-TimesFM-2.5,Drift,0.92,0.86,0.97,0.409,0.345,0.469
-Toto-1.0,Chronos-2,0.26,0.17,0.35,-0.113,-0.154,-0.079
-Toto-1.0,TiRex,0.385,0.29,0.48,-0.026,-0.048,-0.003
-Toto-1.0,TimesFM-2.5,0.43,0.34,0.515,-0.028,-0.054,-0.005
-Toto-1.0,Toto-1.0,0.5,0.5,0.5,0.0,0.0,0.0
-Toto-1.0,Moirai-2.0,0.58,0.49,0.67,0.013,-0.008,0.034
-Toto-1.0,Chronos-Bolt,0.56,0.46,0.645,0.023,-0.005,0.048
-Toto-1.0,TabPFN-TS,0.58,0.49,0.67,0.008,-0.043,0.055
-Toto-1.0,Sundial-Base,0.675,0.585,0.765,0.046,0.007,0.088
-Toto-1.0,Stat. Ensemble,0.73,0.64,0.81,0.149,0.102,0.193
-Toto-1.0,AutoARIMA,0.81,0.73,0.88,0.191,0.149,0.237
-Toto-1.0,AutoTheta,0.82,0.74,0.89,0.194,0.151,0.235
-Toto-1.0,AutoETS,0.79,0.71,0.86,0.266,0.203,0.326
-Toto-1.0,Seasonal Naive,0.91,0.85,0.96,0.282,0.241,0.327
-Toto-1.0,Naive,0.94,0.88,0.98,0.385,0.32,0.442
-Toto-1.0,Drift,0.89,0.82,0.94,0.392,0.328,0.451
-Moirai-2.0,Chronos-2,0.08,0.03,0.14,-0.128,-0.164,-0.094
-Moirai-2.0,TiRex,0.235,0.155,0.315,-0.039,-0.059,-0.021
-Moirai-2.0,TimesFM-2.5,0.29,0.205,0.375,-0.042,-0.064,-0.022
-Moirai-2.0,Toto-1.0,0.42,0.33,0.51,-0.013,-0.036,0.008
-Moirai-2.0,Moirai-2.0,0.5,0.5,0.5,0.0,0.0,0.0
-Moirai-2.0,Chronos-Bolt,0.55,0.465,0.64,0.01,-0.012,0.032
-Moirai-2.0,TabPFN-TS,0.52,0.43,0.62,-0.005,-0.054,0.038
-Moirai-2.0,Sundial-Base,0.685,0.595,0.775,0.033,-0.004,0.067
-Moirai-2.0,Stat. Ensemble,0.73,0.64,0.82,0.138,0.09,0.18
-Moirai-2.0,AutoARIMA,0.81,0.73,0.88,0.18,0.138,0.228
-Moirai-2.0,AutoTheta,0.85,0.77,0.91,0.183,0.144,0.223
-Moirai-2.0,AutoETS,0.76,0.67,0.83,0.256,0.192,0.32
-Moirai-2.0,Seasonal Naive,0.87,0.8,0.93,0.273,0.236,0.314
-Moirai-2.0,Naive,0.91,0.85,0.96,0.377,0.316,0.433
-Moirai-2.0,Drift,0.86,0.79,0.92,0.384,0.32,0.441
-Chronos-Bolt,Chronos-2,0.06,0.02,0.11,-0.139,-0.177,-0.106
-Chronos-Bolt,TiRex,0.235,0.155,0.32,-0.05,-0.072,-0.03
-Chronos-Bolt,TimesFM-2.5,0.29,0.21,0.375,-0.053,-0.077,-0.03
-Chronos-Bolt,Toto-1.0,0.44,0.355,0.54,-0.024,-0.051,0.005
-Chronos-Bolt,Moirai-2.0,0.45,0.36,0.535,-0.01,-0.033,0.012
-Chronos-Bolt,Chronos-Bolt,0.5,0.5,0.5,0.0,0.0,0.0
-Chronos-Bolt,TabPFN-TS,0.52,0.42,0.61,-0.016,-0.062,0.025
-Chronos-Bolt,Sundial-Base,0.605,0.51,0.695,0.024,-0.014,0.057
-Chronos-Bolt,Stat. Ensemble,0.7,0.61,0.79,0.129,0.084,0.174
-Chronos-Bolt,AutoARIMA,0.84,0.76,0.9,0.172,0.132,0.215
-Chronos-Bolt,AutoTheta,0.86,0.79,0.92,0.174,0.137,0.212
-Chronos-Bolt,AutoETS,0.77,0.68,0.85,0.248,0.184,0.315
-Chronos-Bolt,Seasonal Naive,0.9,0.84,0.95,0.265,0.226,0.304
-Chronos-Bolt,Naive,0.94,0.89,0.98,0.37,0.315,0.423
-Chronos-Bolt,Drift,0.9,0.83,0.95,0.378,0.319,0.435
-TabPFN-TS,Chronos-2,0.15,0.08,0.23,-0.122,-0.163,-0.087
-TabPFN-TS,TiRex,0.31,0.22,0.4,-0.034,-0.08,0.01
-TabPFN-TS,TimesFM-2.5,0.33,0.23,0.42,-0.036,-0.088,0.011
-TabPFN-TS,Toto-1.0,0.42,0.33,0.51,-0.008,-0.058,0.041
-TabPFN-TS,Moirai-2.0,0.48,0.38,0.57,0.005,-0.039,0.051
-TabPFN-TS,Chronos-Bolt,0.48,0.39,0.58,0.015,-0.026,0.058
-TabPFN-TS,TabPFN-TS,0.5,0.5,0.5,0.0,0.0,0.0
-TabPFN-TS,Sundial-Base,0.53,0.43,0.63,0.039,-0.001,0.078
-TabPFN-TS,Stat. Ensemble,0.645,0.545,0.735,0.142,0.089,0.19
-TabPFN-TS,AutoARIMA,0.75,0.67,0.83,0.185,0.133,0.241
-TabPFN-TS,AutoTheta,0.75,0.66,0.83,0.187,0.14,0.235
-TabPFN-TS,AutoETS,0.72,0.63,0.81,0.26,0.183,0.332
-TabPFN-TS,Seasonal Naive,0.91,0.855,0.96,0.276,0.231,0.321
-TabPFN-TS,Naive,0.87,0.8,0.93,0.38,0.318,0.442
-TabPFN-TS,Drift,0.87,0.8,0.94,0.388,0.321,0.45
-Sundial-Base,Chronos-2,0.09,0.04,0.15,-0.167,-0.207,-0.128
-Sundial-Base,TiRex,0.195,0.12,0.28,-0.075,-0.115,-0.037
-Sundial-Base,TimesFM-2.5,0.195,0.12,0.27,-0.078,-0.117,-0.042
-Sundial-Base,Toto-1.0,0.325,0.235,0.415,-0.048,-0.097,-0.007
-Sundial-Base,Moirai-2.0,0.315,0.225,0.405,-0.035,-0.072,0.004
-Sundial-Base,Chronos-Bolt,0.395,0.305,0.49,-0.024,-0.061,0.014
-Sundial-Base,TabPFN-TS,0.47,0.37,0.57,-0.04,-0.085,0.001
-Sundial-Base,Sundial-Base,0.5,0.5,0.5,0.0,0.0,0.0
-Sundial-Base,Stat. Ensemble,0.66,0.56,0.76,0.108,0.051,0.16
-Sundial-Base,AutoARIMA,0.76,0.67,0.84,0.152,0.098,0.203
-Sundial-Base,AutoTheta,0.77,0.68,0.85,0.155,0.109,0.199
-Sundial-Base,AutoETS,0.71,0.61,0.8,0.23,0.152,0.3
-Sundial-Base,Seasonal Naive,0.88,0.81,0.94,0.247,0.206,0.288
-Sundial-Base,Naive,0.88,0.8,0.94,0.355,0.289,0.417
-Sundial-Base,Drift,0.83,0.75,0.9,0.363,0.291,0.428
-Stat. Ensemble,Chronos-2,0.14,0.07,0.21,-0.308,-0.383,-0.234
-Stat. Ensemble,TiRex,0.19,0.11,0.27,-0.205,-0.265,-0.152
-Stat. Ensemble,TimesFM-2.5,0.21,0.13,0.29,-0.208,-0.277,-0.144
-Stat. Ensemble,Toto-1.0,0.27,0.19,0.36,-0.175,-0.239,-0.113
-Stat. Ensemble,Moirai-2.0,0.27,0.18,0.36,-0.16,-0.219,-0.099
-Stat. Ensemble,Chronos-Bolt,0.3,0.21,0.39,-0.148,-0.211,-0.091
-Stat. Ensemble,TabPFN-TS,0.355,0.265,0.455,-0.166,-0.235,-0.097
-Stat. Ensemble,Sundial-Base,0.34,0.24,0.44,-0.121,-0.19,-0.054
-Stat. Ensemble,Stat. Ensemble,0.5,0.5,0.5,0.0,0.0,0.0
-Stat. Ensemble,AutoARIMA,0.64,0.545,0.73,0.05,0.02,0.09
-Stat. Ensemble,AutoTheta,0.75,0.67,0.83,0.052,0.025,0.082
-Stat. Ensemble,AutoETS,0.795,0.72,0.87,0.137,0.081,0.194
-Stat. Ensemble,Seasonal Naive,0.795,0.725,0.865,0.157,0.116,0.203
-Stat. Ensemble,Naive,0.85,0.78,0.92,0.277,0.218,0.346
-Stat. Ensemble,Drift,0.89,0.82,0.95,0.286,0.225,0.35
-AutoARIMA,Chronos-2,0.05,0.01,0.1,-0.376,-0.459,-0.298
-AutoARIMA,TiRex,0.1,0.05,0.16,-0.268,-0.339,-0.212
-AutoARIMA,TimesFM-2.5,0.13,0.07,0.2,-0.272,-0.346,-0.212
-AutoARIMA,Toto-1.0,0.19,0.12,0.27,-0.236,-0.311,-0.175
-AutoARIMA,Moirai-2.0,0.19,0.12,0.27,-0.22,-0.295,-0.16
-AutoARIMA,Chronos-Bolt,0.16,0.1,0.24,-0.208,-0.274,-0.152
-AutoARIMA,TabPFN-TS,0.25,0.17,0.33,-0.227,-0.317,-0.153
-AutoARIMA,Sundial-Base,0.24,0.16,0.33,-0.179,-0.254,-0.108
-AutoARIMA,Stat. Ensemble,0.36,0.27,0.455,-0.052,-0.099,-0.02
-AutoARIMA,AutoARIMA,0.5,0.5,0.5,0.0,0.0,0.0
-AutoARIMA,AutoTheta,0.55,0.45,0.64,0.003,-0.039,0.039
-AutoARIMA,AutoETS,0.595,0.5,0.69,0.092,0.024,0.162
-AutoARIMA,Seasonal Naive,0.75,0.665,0.83,0.112,0.068,0.161
-AutoARIMA,Naive,0.77,0.69,0.85,0.239,0.169,0.312
-AutoARIMA,Drift,0.8,0.72,0.87,0.249,0.174,0.324
-AutoTheta,Chronos-2,0.06,0.02,0.11,-0.38,-0.462,-0.308
-AutoTheta,TiRex,0.06,0.02,0.11,-0.272,-0.332,-0.22
-AutoTheta,TimesFM-2.5,0.1,0.05,0.17,-0.275,-0.343,-0.215
-AutoTheta,Toto-1.0,0.18,0.11,0.26,-0.24,-0.307,-0.178
-AutoTheta,Moirai-2.0,0.15,0.09,0.23,-0.224,-0.287,-0.168
-AutoTheta,Chronos-Bolt,0.14,0.08,0.21,-0.211,-0.269,-0.159
-AutoTheta,TabPFN-TS,0.25,0.17,0.34,-0.23,-0.307,-0.163
-AutoTheta,Sundial-Base,0.23,0.15,0.32,-0.183,-0.249,-0.122
-AutoTheta,Stat. Ensemble,0.25,0.17,0.33,-0.055,-0.09,-0.025
-AutoTheta,AutoARIMA,0.45,0.36,0.55,-0.003,-0.041,0.037
-AutoTheta,AutoTheta,0.5,0.5,0.5,0.0,0.0,0.0
-AutoTheta,AutoETS,0.59,0.5,0.69,0.089,0.026,0.155
-AutoTheta,Seasonal Naive,0.78,0.7,0.86,0.11,0.069,0.149
-AutoTheta,Naive,0.83,0.75,0.9,0.237,0.181,0.301
-AutoTheta,Drift,0.82,0.74,0.9,0.247,0.189,0.31
-AutoETS,Chronos-2,0.11,0.05,0.17,-0.515,-0.671,-0.39
-AutoETS,TiRex,0.16,0.09,0.23,-0.397,-0.526,-0.291
-AutoETS,TimesFM-2.5,0.16,0.09,0.24,-0.4,-0.531,-0.295
-AutoETS,Toto-1.0,0.21,0.14,0.29,-0.362,-0.484,-0.254
-AutoETS,Moirai-2.0,0.24,0.17,0.33,-0.344,-0.471,-0.237
-AutoETS,Chronos-Bolt,0.23,0.15,0.32,-0.33,-0.459,-0.225
-AutoETS,TabPFN-TS,0.28,0.19,0.37,-0.351,-0.498,-0.225
-AutoETS,Sundial-Base,0.29,0.2,0.39,-0.299,-0.428,-0.179
-AutoETS,Stat. Ensemble,0.205,0.13,0.28,-0.159,-0.241,-0.088
-AutoETS,AutoARIMA,0.405,0.31,0.5,-0.101,-0.194,-0.024
-AutoETS,AutoTheta,0.41,0.31,0.5,-0.098,-0.183,-0.027
-AutoETS,AutoETS,0.5,0.5,0.5,0.0,0.0,0.0
-AutoETS,Seasonal Naive,0.635,0.54,0.72,0.023,-0.071,0.103
-AutoETS,Naive,0.64,0.55,0.73,0.162,0.086,0.251
-AutoETS,Drift,0.68,0.59,0.77,0.173,0.098,0.258
-Seasonal Naive,Chronos-2,0.02,0.0,0.05,-0.55,-0.648,-0.466
-Seasonal Naive,TiRex,0.03,0.0,0.06,-0.429,-0.512,-0.359
-Seasonal Naive,TimesFM-2.5,0.04,0.01,0.08,-0.433,-0.52,-0.365
-Seasonal Naive,Toto-1.0,0.09,0.04,0.15,-0.393,-0.485,-0.318
-Seasonal Naive,Moirai-2.0,0.13,0.07,0.2,-0.375,-0.458,-0.308
-Seasonal Naive,Chronos-Bolt,0.1,0.05,0.16,-0.361,-0.437,-0.293
-Seasonal Naive,TabPFN-TS,0.09,0.04,0.145,-0.382,-0.472,-0.301
-Seasonal Naive,Sundial-Base,0.12,0.06,0.19,-0.329,-0.405,-0.259
-Seasonal Naive,Stat. Ensemble,0.205,0.135,0.275,-0.186,-0.255,-0.131
-Seasonal Naive,AutoARIMA,0.25,0.17,0.335,-0.127,-0.191,-0.073
-Seasonal Naive,AutoTheta,0.22,0.14,0.3,-0.123,-0.176,-0.074
-Seasonal Naive,AutoETS,0.365,0.28,0.46,-0.023,-0.115,0.067
-Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0
-Seasonal Naive,Naive,0.575,0.48,0.665,0.143,0.065,0.223
-Seasonal Naive,Drift,0.7,0.6,0.79,0.154,0.067,0.238
-Naive,Chronos-2,0.02,0.0,0.05,-0.809,-1.021,-0.634
-Naive,TiRex,0.04,0.01,0.08,-0.667,-0.842,-0.525
-Naive,TimesFM-2.5,0.04,0.01,0.08,-0.671,-0.844,-0.518
-Naive,Toto-1.0,0.06,0.02,0.12,-0.625,-0.792,-0.471
-Naive,Moirai-2.0,0.09,0.04,0.15,-0.604,-0.764,-0.463
-Naive,Chronos-Bolt,0.06,0.02,0.11,-0.588,-0.733,-0.46
-Naive,TabPFN-TS,0.13,0.07,0.2,-0.613,-0.791,-0.467
-Naive,Sundial-Base,0.12,0.06,0.2,-0.55,-0.716,-0.407
-Naive,Stat. Ensemble,0.15,0.08,0.22,-0.383,-0.529,-0.279
-Naive,AutoARIMA,0.23,0.15,0.31,-0.314,-0.454,-0.204
-Naive,AutoTheta,0.17,0.1,0.25,-0.311,-0.432,-0.222
-Naive,AutoETS,0.36,0.27,0.45,-0.194,-0.336,-0.094
-Naive,Seasonal Naive,0.425,0.335,0.52,-0.167,-0.288,-0.069
-Naive,Naive,0.5,0.5,0.5,0.0,0.0,0.0
-Naive,Drift,0.81,0.73,0.89,0.012,-0.012,0.035
-Drift,Chronos-2,0.06,0.02,0.11,-0.831,-1.053,-0.644
-Drift,TiRex,0.06,0.02,0.11,-0.688,-0.859,-0.531
-Drift,TimesFM-2.5,0.08,0.03,0.14,-0.692,-0.883,-0.526
-Drift,Toto-1.0,0.11,0.06,0.18,-0.646,-0.821,-0.487
-Drift,Moirai-2.0,0.14,0.08,0.21,-0.624,-0.789,-0.47
-Drift,Chronos-Bolt,0.1,0.05,0.17,-0.608,-0.77,-0.468
-Drift,TabPFN-TS,0.13,0.06,0.2,-0.633,-0.819,-0.473
-Drift,Sundial-Base,0.17,0.1,0.25,-0.57,-0.747,-0.411
-Drift,Stat. Ensemble,0.11,0.05,0.18,-0.401,-0.539,-0.291
-Drift,AutoARIMA,0.2,0.13,0.28,-0.331,-0.479,-0.21
-Drift,AutoTheta,0.18,0.1,0.26,-0.327,-0.45,-0.233
-Drift,AutoETS,0.32,0.23,0.41,-0.209,-0.347,-0.109
-Drift,Seasonal Naive,0.3,0.21,0.4,-0.181,-0.312,-0.071
-Drift,Naive,0.19,0.11,0.27,-0.013,-0.036,0.012
-Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/pairwise_SQL.csv

@@ -1,226 +0,0 @@
-model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
-Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
-Chronos-2,TiRex,0.72,0.63,0.81,0.082,0.056,0.111
-Chronos-2,TimesFM-2.5,0.76,0.67,0.84,0.088,0.059,0.115
-Chronos-2,Toto-1.0,0.78,0.69,0.86,0.11,0.081,0.141
-Chronos-2,Moirai-2.0,0.93,0.88,0.97,0.131,0.103,0.16
-Chronos-2,Chronos-Bolt,0.94,0.89,0.98,0.137,0.11,0.168
-Chronos-2,TabPFN-TS,0.88,0.81,0.94,0.127,0.097,0.159
-Chronos-2,Sundial-Base,0.96,0.92,0.99,0.208,0.18,0.236
-Chronos-2,Stat. Ensemble,0.95,0.9,0.99,0.34,0.288,0.386
-Chronos-2,AutoARIMA,0.96,0.92,0.99,0.336,0.29,0.38
-Chronos-2,AutoETS,0.94,0.89,0.98,0.562,0.466,0.655
-Chronos-2,AutoTheta,0.99,0.97,1.0,0.442,0.387,0.495
-Chronos-2,Seasonal Naive,1.0,1.0,1.0,0.473,0.427,0.521
-Chronos-2,Naive,1.0,1.0,1.0,0.637,0.583,0.684
-Chronos-2,Drift,0.99,0.97,1.0,0.638,0.581,0.687
-TiRex,Chronos-2,0.28,0.19,0.37,-0.089,-0.125,-0.06
-TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
-TiRex,TimesFM-2.5,0.565,0.465,0.66,0.006,-0.01,0.022
-TiRex,Toto-1.0,0.685,0.595,0.775,0.031,0.009,0.052
-TiRex,Moirai-2.0,0.825,0.75,0.895,0.053,0.036,0.072
-TiRex,Chronos-Bolt,0.835,0.76,0.905,0.06,0.042,0.08
-TiRex,TabPFN-TS,0.72,0.63,0.81,0.049,0.005,0.092
-TiRex,Sundial-Base,0.905,0.85,0.96,0.137,0.107,0.169
-TiRex,Stat. Ensemble,0.92,0.86,0.97,0.281,0.232,0.328
-TiRex,AutoARIMA,0.95,0.9,0.99,0.277,0.237,0.32
-TiRex,AutoETS,0.92,0.86,0.97,0.524,0.417,0.626
-TiRex,AutoTheta,0.99,0.97,1.0,0.393,0.339,0.445
-TiRex,Seasonal Naive,1.0,1.0,1.0,0.426,0.379,0.479
-TiRex,Naive,0.99,0.97,1.0,0.605,0.547,0.653
-TiRex,Drift,0.99,0.97,1.0,0.606,0.545,0.655
-TimesFM-2.5,Chronos-2,0.24,0.16,0.33,-0.096,-0.13,-0.062
-TimesFM-2.5,TiRex,0.435,0.34,0.535,-0.007,-0.023,0.01
-TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
-TimesFM-2.5,Toto-1.0,0.56,0.47,0.66,0.025,0.001,0.048
-TimesFM-2.5,Moirai-2.0,0.74,0.66,0.82,0.047,0.026,0.069
-TimesFM-2.5,Chronos-Bolt,0.72,0.635,0.8,0.054,0.033,0.075
-TimesFM-2.5,TabPFN-TS,0.7,0.61,0.79,0.043,-0.005,0.087
-TimesFM-2.5,Sundial-Base,0.925,0.875,0.97,0.132,0.103,0.161
-TimesFM-2.5,Stat. Ensemble,0.84,0.77,0.91,0.276,0.222,0.327
-TimesFM-2.5,AutoARIMA,0.92,0.86,0.97,0.272,0.232,0.319
-TimesFM-2.5,AutoETS,0.87,0.8,0.93,0.521,0.413,0.624
-TimesFM-2.5,AutoTheta,0.96,0.91,0.99,0.389,0.331,0.446
-TimesFM-2.5,Seasonal Naive,0.99,0.97,1.0,0.422,0.375,0.474
-TimesFM-2.5,Naive,0.99,0.97,1.0,0.602,0.543,0.65
-TimesFM-2.5,Drift,0.97,0.93,1.0,0.604,0.542,0.653
-Toto-1.0,Chronos-2,0.22,0.14,0.31,-0.124,-0.164,-0.088
-Toto-1.0,TiRex,0.315,0.225,0.405,-0.032,-0.054,-0.01
-Toto-1.0,TimesFM-2.5,0.44,0.34,0.53,-0.025,-0.051,-0.001
-Toto-1.0,Toto-1.0,0.5,0.5,0.5,0.0,0.0,0.0
-Toto-1.0,Moirai-2.0,0.6,0.51,0.685,0.023,0.002,0.045
-Toto-1.0,Chronos-Bolt,0.57,0.475,0.655,0.03,0.001,0.055
-Toto-1.0,TabPFN-TS,0.57,0.48,0.66,0.019,-0.033,0.07
-Toto-1.0,Sundial-Base,0.825,0.745,0.895,0.11,0.074,0.147
-Toto-1.0,Stat. Ensemble,0.8,0.72,0.87,0.258,0.2,0.309
-Toto-1.0,AutoARIMA,0.86,0.79,0.92,0.254,0.207,0.305
-Toto-1.0,AutoETS,0.83,0.75,0.9,0.51,0.402,0.614
-Toto-1.0,AutoTheta,0.9,0.83,0.95,0.373,0.31,0.431
-Toto-1.0,Seasonal Naive,0.96,0.92,0.99,0.407,0.354,0.465
-Toto-1.0,Naive,0.98,0.95,1.0,0.592,0.532,0.642
-Toto-1.0,Drift,0.96,0.92,0.99,0.594,0.53,0.645
-Moirai-2.0,Chronos-2,0.07,0.03,0.12,-0.151,-0.19,-0.115
-Moirai-2.0,TiRex,0.175,0.105,0.25,-0.056,-0.077,-0.038
-Moirai-2.0,TimesFM-2.5,0.26,0.18,0.34,-0.05,-0.074,-0.027
-Moirai-2.0,Toto-1.0,0.4,0.315,0.49,-0.024,-0.047,-0.002
-Moirai-2.0,Moirai-2.0,0.5,0.5,0.5,0.0,0.0,0.0
-Moirai-2.0,Chronos-Bolt,0.54,0.455,0.625,0.007,-0.016,0.029
-Moirai-2.0,TabPFN-TS,0.53,0.44,0.63,-0.004,-0.055,0.042
-Moirai-2.0,Sundial-Base,0.855,0.78,0.92,0.089,0.054,0.123
-Moirai-2.0,Stat. Ensemble,0.79,0.71,0.86,0.24,0.187,0.292
-Moirai-2.0,AutoARIMA,0.87,0.8,0.93,0.236,0.189,0.286
-Moirai-2.0,AutoETS,0.82,0.74,0.89,0.5,0.382,0.609
-Moirai-2.0,AutoTheta,0.88,0.81,0.93,0.358,0.299,0.415
-Moirai-2.0,Seasonal Naive,0.94,0.89,0.98,0.393,0.343,0.448
-Moirai-2.0,Naive,0.95,0.9,0.99,0.583,0.523,0.634
-Moirai-2.0,Drift,0.94,0.88,0.98,0.584,0.519,0.636
-Chronos-Bolt,Chronos-2,0.06,0.02,0.11,-0.159,-0.201,-0.123
-Chronos-Bolt,TiRex,0.165,0.095,0.24,-0.064,-0.088,-0.044
-Chronos-Bolt,TimesFM-2.5,0.28,0.2,0.365,-0.057,-0.081,-0.034
-Chronos-Bolt,Toto-1.0,0.43,0.345,0.525,-0.031,-0.059,-0.001
-Chronos-Bolt,Moirai-2.0,0.46,0.375,0.545,-0.007,-0.03,0.016
-Chronos-Bolt,Chronos-Bolt,0.5,0.5,0.5,0.0,0.0,0.0
-Chronos-Bolt,TabPFN-TS,0.53,0.43,0.63,-0.011,-0.059,0.031
-Chronos-Bolt,Sundial-Base,0.815,0.74,0.89,0.082,0.045,0.116
-Chronos-Bolt,Stat. Ensemble,0.78,0.69,0.85,0.235,0.184,0.284
-Chronos-Bolt,AutoARIMA,0.87,0.8,0.92,0.231,0.188,0.277
-Chronos-Bolt,AutoETS,0.8,0.72,0.87,0.496,0.379,0.605
-Chronos-Bolt,AutoTheta,0.92,0.86,0.97,0.354,0.3,0.409
-Chronos-Bolt,Seasonal Naive,0.96,0.92,0.99,0.389,0.342,0.441
-Chronos-Bolt,Naive,0.98,0.95,1.0,0.58,0.524,0.628
-Chronos-Bolt,Drift,0.97,0.93,1.0,0.581,0.52,0.63
-TabPFN-TS,Chronos-2,0.12,0.06,0.19,-0.146,-0.189,-0.108
-TabPFN-TS,TiRex,0.28,0.19,0.37,-0.052,-0.101,-0.005
-TabPFN-TS,TimesFM-2.5,0.3,0.21,0.39,-0.045,-0.095,0.005
-TabPFN-TS,Toto-1.0,0.43,0.34,0.52,-0.02,-0.076,0.032
-TabPFN-TS,Moirai-2.0,0.47,0.37,0.56,0.004,-0.044,0.052
-TabPFN-TS,Chronos-Bolt,0.47,0.37,0.57,0.011,-0.032,0.055
-TabPFN-TS,TabPFN-TS,0.5,0.5,0.5,0.0,0.0,0.0
-TabPFN-TS,Sundial-Base,0.72,0.63,0.8,0.093,0.05,0.135
-TabPFN-TS,Stat. Ensemble,0.735,0.645,0.815,0.243,0.19,0.297
-TabPFN-TS,AutoARIMA,0.81,0.73,0.88,0.239,0.189,0.292
-TabPFN-TS,AutoETS,0.75,0.66,0.83,0.503,0.387,0.611
-TabPFN-TS,AutoTheta,0.89,0.82,0.95,0.361,0.302,0.422
-TabPFN-TS,Seasonal Naive,0.96,0.92,0.99,0.396,0.346,0.447
-TabPFN-TS,Naive,0.96,0.92,0.99,0.584,0.526,0.636
-TabPFN-TS,Drift,0.92,0.86,0.97,0.586,0.521,0.639
-Sundial-Base,Chronos-2,0.04,0.01,0.08,-0.263,-0.309,-0.219
-Sundial-Base,TiRex,0.095,0.04,0.15,-0.159,-0.203,-0.119
-Sundial-Base,TimesFM-2.5,0.075,0.03,0.125,-0.152,-0.191,-0.115
-Sundial-Base,Toto-1.0,0.175,0.105,0.255,-0.124,-0.172,-0.079
-Sundial-Base,Moirai-2.0,0.145,0.08,0.22,-0.097,-0.14,-0.057
-Sundial-Base,Chronos-Bolt,0.185,0.11,0.26,-0.09,-0.132,-0.047
-Sundial-Base,TabPFN-TS,0.28,0.2,0.37,-0.102,-0.155,-0.053
-Sundial-Base,Sundial-Base,0.5,0.5,0.5,0.0,0.0,0.0
-Sundial-Base,Stat. Ensemble,0.64,0.55,0.73,0.166,0.096,0.23
-Sundial-Base,AutoARIMA,0.71,0.61,0.79,0.162,0.102,0.22
-Sundial-Base,AutoETS,0.7,0.61,0.78,0.453,0.323,0.576
-Sundial-Base,AutoTheta,0.74,0.65,0.82,0.296,0.226,0.364
-Sundial-Base,Seasonal Naive,0.91,0.85,0.96,0.334,0.281,0.392
-Sundial-Base,Naive,0.9,0.84,0.96,0.542,0.473,0.598
-Sundial-Base,Drift,0.84,0.76,0.91,0.543,0.47,0.602
-Stat. Ensemble,Chronos-2,0.05,0.01,0.1,-0.514,-0.63,-0.404
-Stat. Ensemble,TiRex,0.08,0.03,0.14,-0.39,-0.488,-0.302
-Stat. Ensemble,TimesFM-2.5,0.16,0.09,0.23,-0.381,-0.485,-0.285
-Stat. Ensemble,Toto-1.0,0.2,0.13,0.28,-0.347,-0.448,-0.25
-Stat. Ensemble,Moirai-2.0,0.21,0.14,0.29,-0.316,-0.412,-0.23
-Stat. Ensemble,Chronos-Bolt,0.22,0.15,0.31,-0.307,-0.396,-0.226
-Stat. Ensemble,TabPFN-TS,0.265,0.185,0.355,-0.322,-0.423,-0.234
-Stat. Ensemble,Sundial-Base,0.36,0.27,0.45,-0.199,-0.298,-0.107
-Stat. Ensemble,Stat. Ensemble,0.5,0.5,0.5,0.0,0.0,0.0
-Stat. Ensemble,AutoARIMA,0.51,0.42,0.605,-0.005,-0.049,0.041
-Stat. Ensemble,AutoETS,0.765,0.68,0.845,0.365,0.22,0.501
-Stat. Ensemble,AutoTheta,0.88,0.82,0.94,0.156,0.108,0.208
-Stat. Ensemble,Seasonal Naive,0.795,0.725,0.87,0.202,0.148,0.257
-Stat. Ensemble,Naive,0.93,0.88,0.98,0.451,0.395,0.503
-Stat. Ensemble,Drift,0.95,0.9,0.99,0.452,0.394,0.506
-AutoARIMA,Chronos-2,0.04,0.01,0.08,-0.507,-0.612,-0.409
-AutoARIMA,TiRex,0.05,0.01,0.1,-0.383,-0.47,-0.311
-AutoARIMA,TimesFM-2.5,0.08,0.03,0.14,-0.374,-0.468,-0.301
-AutoARIMA,Toto-1.0,0.14,0.08,0.21,-0.341,-0.439,-0.261
-AutoARIMA,Moirai-2.0,0.13,0.07,0.2,-0.309,-0.401,-0.233
-AutoARIMA,Chronos-Bolt,0.13,0.08,0.2,-0.3,-0.383,-0.232
-AutoARIMA,TabPFN-TS,0.19,0.12,0.27,-0.315,-0.412,-0.233
-AutoARIMA,Sundial-Base,0.29,0.21,0.39,-0.193,-0.282,-0.114
-AutoARIMA,Stat. Ensemble,0.49,0.395,0.58,0.005,-0.043,0.047
-AutoARIMA,AutoARIMA,0.5,0.5,0.5,0.0,0.0,0.0
-AutoARIMA,AutoETS,0.695,0.615,0.785,0.359,0.211,0.496
-AutoARIMA,AutoTheta,0.77,0.69,0.85,0.16,0.099,0.216
-AutoARIMA,Seasonal Naive,0.88,0.82,0.935,0.206,0.154,0.263
-AutoARIMA,Naive,0.92,0.86,0.97,0.454,0.388,0.512
-AutoARIMA,Drift,0.89,0.82,0.95,0.455,0.384,0.516
-AutoETS,Chronos-2,0.06,0.02,0.11,-1.285,-1.897,-0.873
-AutoETS,TiRex,0.08,0.03,0.14,-1.102,-1.673,-0.714
-AutoETS,TimesFM-2.5,0.13,0.07,0.2,-1.087,-1.662,-0.703
-AutoETS,Toto-1.0,0.17,0.1,0.25,-1.041,-1.59,-0.672
-AutoETS,Moirai-2.0,0.18,0.11,0.26,-0.999,-1.559,-0.619
-AutoETS,Chronos-Bolt,0.2,0.13,0.28,-0.983,-1.531,-0.609
-AutoETS,TabPFN-TS,0.25,0.17,0.34,-1.011,-1.572,-0.632
-AutoETS,Sundial-Base,0.3,0.22,0.39,-0.828,-1.359,-0.478
-AutoETS,Stat. Ensemble,0.235,0.155,0.32,-0.575,-1.003,-0.282
-AutoETS,AutoARIMA,0.305,0.215,0.385,-0.56,-0.983,-0.268
-AutoETS,AutoETS,0.5,0.5,0.5,0.0,0.0,0.0
-AutoETS,AutoTheta,0.59,0.49,0.69,-0.345,-0.709,-0.081
-AutoETS,Seasonal Naive,0.635,0.545,0.725,-0.268,-0.644,-0.013
-AutoETS,Naive,0.77,0.69,0.85,0.104,-0.173,0.297
-AutoETS,Drift,0.81,0.73,0.88,0.103,-0.161,0.297
-AutoTheta,Chronos-2,0.01,0.0,0.03,-0.793,-0.98,-0.63
-AutoTheta,TiRex,0.01,0.0,0.03,-0.646,-0.803,-0.513
-AutoTheta,TimesFM-2.5,0.04,0.01,0.09,-0.636,-0.805,-0.495
-AutoTheta,Toto-1.0,0.1,0.05,0.17,-0.596,-0.759,-0.449
-AutoTheta,Moirai-2.0,0.12,0.07,0.19,-0.558,-0.711,-0.427
-AutoTheta,Chronos-Bolt,0.08,0.03,0.14,-0.547,-0.691,-0.429
-AutoTheta,TabPFN-TS,0.11,0.05,0.18,-0.565,-0.731,-0.433
-AutoTheta,Sundial-Base,0.26,0.18,0.35,-0.42,-0.572,-0.292
-AutoTheta,Stat. Ensemble,0.12,0.06,0.18,-0.184,-0.263,-0.121
-AutoTheta,AutoARIMA,0.23,0.15,0.31,-0.19,-0.276,-0.11
-AutoTheta,AutoETS,0.41,0.31,0.51,0.257,0.075,0.415
-AutoTheta,AutoTheta,0.5,0.5,0.5,0.0,0.0,0.0
-AutoTheta,Seasonal Naive,0.68,0.58,0.77,0.055,-0.038,0.135
-AutoTheta,Naive,0.83,0.75,0.89,0.35,0.293,0.403
-AutoTheta,Drift,0.8,0.71,0.87,0.351,0.288,0.408
-Seasonal Naive,Chronos-2,0.0,0.0,0.0,-0.897,-1.087,-0.745
-Seasonal Naive,TiRex,0.0,0.0,0.0,-0.741,-0.919,-0.611
-Seasonal Naive,TimesFM-2.5,0.01,0.0,0.03,-0.73,-0.902,-0.6
-Seasonal Naive,Toto-1.0,0.04,0.01,0.08,-0.688,-0.867,-0.548
-Seasonal Naive,Moirai-2.0,0.06,0.02,0.11,-0.648,-0.813,-0.522
-Seasonal Naive,Chronos-Bolt,0.04,0.01,0.08,-0.636,-0.788,-0.52
-Seasonal Naive,TabPFN-TS,0.04,0.01,0.08,-0.655,-0.809,-0.529
-Seasonal Naive,Sundial-Base,0.09,0.04,0.15,-0.502,-0.645,-0.39
-Seasonal Naive,Stat. Ensemble,0.205,0.13,0.275,-0.253,-0.347,-0.174
-Seasonal Naive,AutoARIMA,0.12,0.065,0.18,-0.259,-0.356,-0.182
-Seasonal Naive,AutoETS,0.365,0.275,0.455,0.211,0.013,0.392
-Seasonal Naive,AutoTheta,0.32,0.23,0.42,-0.058,-0.156,0.037
-Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0
-Seasonal Naive,Naive,0.715,0.64,0.785,0.312,0.229,0.386
-Seasonal Naive,Drift,0.82,0.74,0.89,0.314,0.222,0.396
-Naive,Chronos-2,0.0,0.0,0.0,-1.758,-2.16,-1.399
-Naive,TiRex,0.01,0.0,0.03,-1.532,-1.88,-1.209
-Naive,TimesFM-2.5,0.01,0.0,0.03,-1.516,-1.859,-1.189
-Naive,Toto-1.0,0.02,0.0,0.05,-1.454,-1.793,-1.135
-Naive,Moirai-2.0,0.05,0.01,0.1,-1.397,-1.731,-1.095
-Naive,Chronos-Bolt,0.02,0.0,0.05,-1.379,-1.689,-1.099
-Naive,TabPFN-TS,0.04,0.01,0.08,-1.407,-1.747,-1.111
-Naive,Sundial-Base,0.1,0.04,0.16,-1.184,-1.49,-0.898
-Naive,Stat. Ensemble,0.07,0.02,0.12,-0.821,-1.014,-0.653
-Naive,AutoARIMA,0.08,0.03,0.14,-0.83,-1.05,-0.635
-Naive,AutoETS,0.23,0.15,0.31,-0.116,-0.422,0.148
-Naive,AutoTheta,0.17,0.11,0.25,-0.538,-0.676,-0.414
-Naive,Seasonal Naive,0.285,0.215,0.36,-0.454,-0.628,-0.298
-Naive,Naive,0.5,0.5,0.5,0.0,0.0,0.0
-Naive,Drift,0.85,0.78,0.92,0.003,-0.02,0.022
-Drift,Chronos-2,0.01,0.0,0.03,-1.765,-2.19,-1.384
-Drift,TiRex,0.01,0.0,0.03,-1.539,-1.898,-1.199
-Drift,TimesFM-2.5,0.03,0.0,0.07,-1.522,-1.883,-1.184
-Drift,Toto-1.0,0.04,0.01,0.08,-1.46,-1.816,-1.126
-Drift,Moirai-2.0,0.06,0.02,0.12,-1.403,-1.749,-1.079
-Drift,Chronos-Bolt,0.03,0.0,0.07,-1.386,-1.705,-1.084
-Drift,TabPFN-TS,0.08,0.03,0.14,-1.413,-1.773,-1.088
-Drift,Sundial-Base,0.16,0.09,0.24,-1.19,-1.513,-0.886
-Drift,Stat. Ensemble,0.05,0.01,0.1,-0.826,-1.023,-0.65
-Drift,AutoARIMA,0.11,0.05,0.18,-0.835,-1.064,-0.623
-Drift,AutoETS,0.19,0.12,0.27,-0.115,-0.423,0.139
-Drift,AutoTheta,0.2,0.13,0.29,-0.542,-0.69,-0.404
-Drift,Seasonal Naive,0.18,0.11,0.26,-0.458,-0.656,-0.285
-Drift,Naive,0.15,0.08,0.22,-0.003,-0.023,0.02
-Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/pairwise_WAPE.csv

@@ -1,226 +0,0 @@
-model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
-Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
-Chronos-2,TimesFM-2.5,0.68,0.59,0.76,0.086,0.054,0.118
-Chronos-2,TiRex,0.73,0.64,0.81,0.088,0.057,0.12
-Chronos-2,Toto-1.0,0.68,0.58,0.76,0.116,0.082,0.153
-Chronos-2,TabPFN-TS,0.82,0.74,0.89,0.091,0.058,0.121
-Chronos-2,Moirai-2.0,0.84,0.76,0.91,0.126,0.096,0.159
-Chronos-2,Chronos-Bolt,0.89,0.82,0.95,0.137,0.105,0.171
-Chronos-2,Sundial-Base,0.93,0.88,0.98,0.167,0.134,0.2
-Chronos-2,Stat. Ensemble,0.91,0.85,0.96,0.264,0.209,0.312
-Chronos-2,AutoARIMA,0.92,0.86,0.97,0.301,0.251,0.35
-Chronos-2,AutoETS,0.88,0.81,0.94,0.367,0.293,0.433
-Chronos-2,AutoTheta,0.94,0.89,0.98,0.297,0.247,0.344
-Chronos-2,Naive,0.93,0.88,0.97,0.429,0.373,0.482
-Chronos-2,Seasonal Naive,0.95,0.9,0.99,0.394,0.349,0.44
-Chronos-2,Drift,0.93,0.88,0.97,0.442,0.383,0.498
-TimesFM-2.5,Chronos-2,0.32,0.24,0.41,-0.094,-0.133,-0.057
-TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
-TimesFM-2.5,TiRex,0.485,0.39,0.58,0.002,-0.014,0.02
-TimesFM-2.5,Toto-1.0,0.53,0.445,0.62,0.033,0.006,0.06
-TimesFM-2.5,TabPFN-TS,0.63,0.53,0.72,0.005,-0.04,0.051
-TimesFM-2.5,Moirai-2.0,0.7,0.615,0.78,0.044,0.019,0.069
-TimesFM-2.5,Chronos-Bolt,0.73,0.65,0.81,0.056,0.034,0.079
-TimesFM-2.5,Sundial-Base,0.825,0.75,0.9,0.089,0.052,0.125
-TimesFM-2.5,Stat. Ensemble,0.84,0.76,0.91,0.195,0.146,0.244
-TimesFM-2.5,AutoARIMA,0.91,0.85,0.97,0.236,0.189,0.285
-TimesFM-2.5,AutoETS,0.85,0.78,0.91,0.307,0.237,0.376
-TimesFM-2.5,AutoTheta,0.92,0.86,0.97,0.231,0.186,0.275
-TimesFM-2.5,Naive,0.93,0.87,0.98,0.375,0.321,0.43
-TimesFM-2.5,Seasonal Naive,0.95,0.9,0.99,0.337,0.293,0.386
-TimesFM-2.5,Drift,0.92,0.86,0.97,0.39,0.336,0.445
-TiRex,Chronos-2,0.27,0.19,0.36,-0.097,-0.137,-0.06
-TiRex,TimesFM-2.5,0.515,0.42,0.61,-0.002,-0.02,0.014
-TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
-TiRex,Toto-1.0,0.625,0.53,0.72,0.03,0.007,0.051
-TiRex,TabPFN-TS,0.65,0.56,0.74,0.003,-0.045,0.052
-TiRex,Moirai-2.0,0.695,0.615,0.79,0.042,0.023,0.065
-TiRex,Chronos-Bolt,0.745,0.66,0.83,0.054,0.032,0.077
-TiRex,Sundial-Base,0.775,0.695,0.85,0.087,0.048,0.126
-TiRex,Stat. Ensemble,0.82,0.73,0.89,0.193,0.146,0.242
-TiRex,AutoARIMA,0.88,0.81,0.94,0.234,0.189,0.282
-TiRex,AutoETS,0.84,0.76,0.91,0.306,0.233,0.374
-TiRex,AutoTheta,0.93,0.88,0.98,0.229,0.187,0.272
-TiRex,Naive,0.93,0.88,0.98,0.374,0.321,0.429
-TiRex,Seasonal Naive,0.95,0.9,0.99,0.336,0.29,0.384
-TiRex,Drift,0.91,0.85,0.96,0.388,0.335,0.443
-Toto-1.0,Chronos-2,0.32,0.24,0.42,-0.131,-0.18,-0.089
-Toto-1.0,TimesFM-2.5,0.47,0.38,0.555,-0.034,-0.064,-0.007
-Toto-1.0,TiRex,0.375,0.28,0.47,-0.031,-0.054,-0.007
-Toto-1.0,Toto-1.0,0.5,0.5,0.5,0.0,0.0,0.0
-Toto-1.0,TabPFN-TS,0.56,0.47,0.65,-0.028,-0.083,0.024
-Toto-1.0,Moirai-2.0,0.6,0.5,0.69,0.012,-0.012,0.036
-Toto-1.0,Chronos-Bolt,0.6,0.505,0.685,0.024,-0.004,0.052
-Toto-1.0,Sundial-Base,0.665,0.575,0.75,0.058,0.011,0.103
-Toto-1.0,Stat. Ensemble,0.69,0.59,0.78,0.168,0.115,0.219
-Toto-1.0,AutoARIMA,0.82,0.74,0.89,0.21,0.157,0.261
-Toto-1.0,AutoETS,0.77,0.68,0.84,0.284,0.208,0.353
-Toto-1.0,AutoTheta,0.87,0.8,0.93,0.205,0.158,0.252
-Toto-1.0,Naive,0.91,0.85,0.96,0.354,0.296,0.408
-Toto-1.0,Seasonal Naive,0.92,0.86,0.97,0.315,0.266,0.365
-Toto-1.0,Drift,0.9,0.83,0.96,0.369,0.315,0.424
-TabPFN-TS,Chronos-2,0.18,0.11,0.26,-0.1,-0.138,-0.061
-TabPFN-TS,TimesFM-2.5,0.37,0.28,0.47,-0.005,-0.054,0.039
-TabPFN-TS,TiRex,0.35,0.26,0.44,-0.003,-0.055,0.043
-TabPFN-TS,Toto-1.0,0.44,0.35,0.53,0.027,-0.024,0.076
-TabPFN-TS,TabPFN-TS,0.5,0.5,0.5,0.0,0.0,0.0
-TabPFN-TS,Moirai-2.0,0.52,0.42,0.62,0.039,-0.012,0.085
-TabPFN-TS,Chronos-Bolt,0.53,0.44,0.62,0.051,0.006,0.094
-TabPFN-TS,Sundial-Base,0.66,0.56,0.75,0.084,0.041,0.126
-TabPFN-TS,Stat. Ensemble,0.775,0.695,0.855,0.19,0.134,0.242
-TabPFN-TS,AutoARIMA,0.83,0.76,0.9,0.232,0.178,0.283
-TabPFN-TS,AutoETS,0.77,0.69,0.85,0.303,0.23,0.382
-TabPFN-TS,AutoTheta,0.84,0.76,0.9,0.227,0.18,0.277
-TabPFN-TS,Naive,0.91,0.85,0.96,0.372,0.316,0.432
-TabPFN-TS,Seasonal Naive,0.94,0.89,0.98,0.334,0.29,0.379
-TabPFN-TS,Drift,0.89,0.83,0.95,0.386,0.328,0.447
-Moirai-2.0,Chronos-2,0.16,0.09,0.24,-0.145,-0.189,-0.106
-Moirai-2.0,TimesFM-2.5,0.3,0.22,0.385,-0.046,-0.075,-0.02
-Moirai-2.0,TiRex,0.305,0.21,0.385,-0.044,-0.069,-0.023
-Moirai-2.0,Toto-1.0,0.4,0.31,0.5,-0.012,-0.038,0.012
-Moirai-2.0,TabPFN-TS,0.48,0.38,0.58,-0.041,-0.093,0.012
-Moirai-2.0,Moirai-2.0,0.5,0.5,0.5,0.0,0.0,0.0
-Moirai-2.0,Chronos-Bolt,0.56,0.48,0.645,0.013,-0.011,0.036
-Moirai-2.0,Sundial-Base,0.665,0.57,0.76,0.047,0.005,0.087
-Moirai-2.0,Stat. Ensemble,0.74,0.65,0.82,0.157,0.105,0.208
-Moirai-2.0,AutoARIMA,0.83,0.75,0.9,0.2,0.148,0.254
-Moirai-2.0,AutoETS,0.77,0.68,0.85,0.275,0.196,0.35
-Moirai-2.0,AutoTheta,0.88,0.81,0.94,0.196,0.147,0.243
-Moirai-2.0,Naive,0.87,0.8,0.93,0.346,0.29,0.403
-Moirai-2.0,Seasonal Naive,0.87,0.8,0.93,0.307,0.258,0.357
-Moirai-2.0,Drift,0.87,0.8,0.93,0.361,0.303,0.417
-Chronos-Bolt,Chronos-2,0.11,0.05,0.18,-0.159,-0.207,-0.117
-Chronos-Bolt,TimesFM-2.5,0.27,0.19,0.35,-0.06,-0.086,-0.035
-Chronos-Bolt,TiRex,0.255,0.17,0.34,-0.057,-0.083,-0.033
-Chronos-Bolt,Toto-1.0,0.4,0.315,0.495,-0.025,-0.055,0.004
-Chronos-Bolt,TabPFN-TS,0.47,0.38,0.56,-0.054,-0.104,-0.006
-Chronos-Bolt,Moirai-2.0,0.44,0.355,0.52,-0.013,-0.038,0.011
-Chronos-Bolt,Chronos-Bolt,0.5,0.5,0.5,0.0,0.0,0.0
-Chronos-Bolt,Sundial-Base,0.635,0.54,0.73,0.034,-0.006,0.071
-Chronos-Bolt,Stat. Ensemble,0.74,0.64,0.82,0.147,0.096,0.198
-Chronos-Bolt,AutoARIMA,0.85,0.77,0.92,0.19,0.137,0.242
-Chronos-Bolt,AutoETS,0.77,0.68,0.85,0.266,0.19,0.341
-Chronos-Bolt,AutoTheta,0.92,0.87,0.97,0.185,0.139,0.232
-Chronos-Bolt,Naive,0.89,0.83,0.95,0.338,0.28,0.394
-Chronos-Bolt,Seasonal Naive,0.91,0.85,0.96,0.298,0.252,0.345
-Chronos-Bolt,Drift,0.9,0.84,0.95,0.353,0.296,0.41
-Sundial-Base,Chronos-2,0.07,0.02,0.12,-0.2,-0.25,-0.154
-Sundial-Base,TimesFM-2.5,0.175,0.1,0.25,-0.097,-0.143,-0.055
-Sundial-Base,TiRex,0.225,0.15,0.305,-0.095,-0.144,-0.05
-Sundial-Base,Toto-1.0,0.335,0.25,0.425,-0.062,-0.115,-0.012
-Sundial-Base,TabPFN-TS,0.34,0.25,0.44,-0.092,-0.144,-0.042
-Sundial-Base,Moirai-2.0,0.335,0.24,0.43,-0.049,-0.095,-0.005
-Sundial-Base,Chronos-Bolt,0.365,0.27,0.46,-0.036,-0.076,0.006
-Sundial-Base,Sundial-Base,0.5,0.5,0.5,0.0,0.0,0.0
-Sundial-Base,Stat. Ensemble,0.64,0.54,0.73,0.116,0.05,0.174
-Sundial-Base,AutoARIMA,0.76,0.67,0.84,0.161,0.097,0.218
-Sundial-Base,AutoETS,0.68,0.58,0.77,0.24,0.149,0.317
-Sundial-Base,AutoTheta,0.74,0.64,0.82,0.156,0.099,0.206
-Sundial-Base,Naive,0.82,0.74,0.89,0.315,0.248,0.38
-Sundial-Base,Seasonal Naive,0.88,0.81,0.94,0.273,0.225,0.319
-Sundial-Base,Drift,0.81,0.73,0.88,0.33,0.261,0.398
-Stat. Ensemble,Chronos-2,0.09,0.04,0.15,-0.358,-0.454,-0.265
-Stat. Ensemble,TimesFM-2.5,0.16,0.09,0.24,-0.242,-0.322,-0.171
-Stat. Ensemble,TiRex,0.18,0.11,0.27,-0.239,-0.319,-0.171
-Stat. Ensemble,Toto-1.0,0.31,0.22,0.41,-0.201,-0.28,-0.13
-Stat. Ensemble,TabPFN-TS,0.225,0.145,0.305,-0.235,-0.32,-0.155
-Stat. Ensemble,Moirai-2.0,0.26,0.18,0.35,-0.187,-0.263,-0.117
-Stat. Ensemble,Chronos-Bolt,0.26,0.18,0.36,-0.172,-0.246,-0.107
-Stat. Ensemble,Sundial-Base,0.36,0.27,0.46,-0.132,-0.211,-0.052
-Stat. Ensemble,Stat. Ensemble,0.5,0.5,0.5,0.0,0.0,0.0
-Stat. Ensemble,AutoARIMA,0.65,0.56,0.74,0.051,0.02,0.086
-Stat. Ensemble,AutoETS,0.785,0.7,0.86,0.14,0.071,0.209
-Stat. Ensemble,AutoTheta,0.81,0.73,0.88,0.045,0.009,0.078
-Stat. Ensemble,Naive,0.84,0.77,0.91,0.224,0.169,0.277
-Stat. Ensemble,Seasonal Naive,0.835,0.77,0.9,0.177,0.138,0.221
-Stat. Ensemble,Drift,0.86,0.79,0.92,0.242,0.192,0.293
-AutoARIMA,Chronos-2,0.08,0.03,0.14,-0.431,-0.538,-0.335
-AutoARIMA,TimesFM-2.5,0.09,0.03,0.15,-0.308,-0.399,-0.234
-AutoARIMA,TiRex,0.12,0.06,0.19,-0.305,-0.393,-0.233
-AutoARIMA,Toto-1.0,0.18,0.11,0.26,-0.266,-0.353,-0.187
-AutoARIMA,TabPFN-TS,0.17,0.1,0.24,-0.301,-0.395,-0.217
-AutoARIMA,Moirai-2.0,0.17,0.1,0.25,-0.251,-0.341,-0.173
-AutoARIMA,Chronos-Bolt,0.15,0.08,0.23,-0.235,-0.319,-0.159
-AutoARIMA,Sundial-Base,0.24,0.16,0.33,-0.192,-0.278,-0.107
-AutoARIMA,Stat. Ensemble,0.35,0.26,0.44,-0.054,-0.095,-0.02
-AutoARIMA,AutoARIMA,0.5,0.5,0.5,0.0,0.0,0.0
-AutoARIMA,AutoETS,0.555,0.46,0.655,0.093,0.015,0.175
-AutoARIMA,AutoTheta,0.52,0.42,0.62,-0.006,-0.057,0.039
-AutoARIMA,Naive,0.72,0.63,0.8,0.183,0.118,0.242
-AutoARIMA,Seasonal Naive,0.8,0.735,0.875,0.133,0.09,0.178
-AutoARIMA,Drift,0.75,0.65,0.83,0.201,0.14,0.261
-AutoETS,Chronos-2,0.12,0.06,0.19,-0.579,-0.764,-0.414
-AutoETS,TimesFM-2.5,0.15,0.09,0.22,-0.443,-0.602,-0.31
-AutoETS,TiRex,0.16,0.09,0.24,-0.44,-0.598,-0.303
-AutoETS,Toto-1.0,0.23,0.16,0.32,-0.396,-0.547,-0.263
-AutoETS,TabPFN-TS,0.23,0.15,0.31,-0.436,-0.617,-0.299
-AutoETS,Moirai-2.0,0.23,0.15,0.32,-0.38,-0.538,-0.243
-AutoETS,Chronos-Bolt,0.23,0.15,0.32,-0.362,-0.518,-0.235
-AutoETS,Sundial-Base,0.32,0.23,0.42,-0.315,-0.464,-0.175
-AutoETS,Stat. Ensemble,0.215,0.14,0.3,-0.162,-0.264,-0.077
-AutoETS,AutoARIMA,0.445,0.345,0.54,-0.103,-0.212,-0.015
-AutoETS,AutoETS,0.5,0.5,0.5,0.0,0.0,0.0
-AutoETS,AutoTheta,0.47,0.37,0.56,-0.11,-0.214,-0.027
-AutoETS,Naive,0.62,0.53,0.72,0.098,0.018,0.177
-AutoETS,Seasonal Naive,0.685,0.585,0.775,0.043,-0.061,0.128
-AutoETS,Drift,0.74,0.66,0.82,0.119,0.042,0.194
-AutoTheta,Chronos-2,0.06,0.02,0.11,-0.423,-0.524,-0.328
-AutoTheta,TimesFM-2.5,0.08,0.03,0.14,-0.301,-0.38,-0.229
-AutoTheta,TiRex,0.07,0.02,0.12,-0.297,-0.374,-0.23
-AutoTheta,Toto-1.0,0.13,0.07,0.2,-0.258,-0.337,-0.188
-AutoTheta,TabPFN-TS,0.16,0.1,0.24,-0.294,-0.383,-0.22
-AutoTheta,Moirai-2.0,0.12,0.06,0.19,-0.243,-0.32,-0.172
-AutoTheta,Chronos-Bolt,0.08,0.03,0.13,-0.227,-0.302,-0.161
-AutoTheta,Sundial-Base,0.26,0.18,0.36,-0.185,-0.26,-0.11
-AutoTheta,Stat. Ensemble,0.19,0.12,0.27,-0.047,-0.085,-0.009
-AutoTheta,AutoARIMA,0.48,0.38,0.58,0.006,-0.041,0.054
-AutoTheta,AutoETS,0.53,0.44,0.63,0.099,0.026,0.177
-AutoTheta,AutoTheta,0.5,0.5,0.5,0.0,0.0,0.0
-AutoTheta,Naive,0.76,0.68,0.84,0.188,0.138,0.239
-AutoTheta,Seasonal Naive,0.78,0.69,0.86,0.138,0.099,0.18
-AutoTheta,Drift,0.78,0.7,0.86,0.206,0.158,0.26
-Naive,Chronos-2,0.07,0.03,0.12,-0.751,-0.93,-0.594
-Naive,TimesFM-2.5,0.07,0.02,0.13,-0.601,-0.753,-0.473
-Naive,TiRex,0.07,0.02,0.12,-0.597,-0.75,-0.473
-Naive,Toto-1.0,0.09,0.04,0.15,-0.549,-0.689,-0.421
-Naive,TabPFN-TS,0.09,0.04,0.15,-0.592,-0.761,-0.461
-Naive,Moirai-2.0,0.13,0.07,0.2,-0.53,-0.674,-0.408
-Naive,Chronos-Bolt,0.11,0.05,0.17,-0.511,-0.649,-0.39
-Naive,Sundial-Base,0.18,0.11,0.26,-0.459,-0.613,-0.331
-Naive,Stat. Ensemble,0.16,0.09,0.23,-0.289,-0.383,-0.203
-Naive,AutoARIMA,0.28,0.2,0.37,-0.223,-0.319,-0.134
-Naive,AutoETS,0.38,0.28,0.47,-0.109,-0.215,-0.018
-Naive,AutoTheta,0.24,0.16,0.32,-0.231,-0.314,-0.161
-Naive,Naive,0.5,0.5,0.5,0.0,0.0,0.0
-Naive,Seasonal Naive,0.475,0.39,0.565,-0.061,-0.146,0.017
-Naive,Drift,0.84,0.77,0.91,0.023,-0.005,0.048
-Seasonal Naive,Chronos-2,0.05,0.01,0.1,-0.65,-0.787,-0.536
-Seasonal Naive,TimesFM-2.5,0.05,0.01,0.1,-0.509,-0.63,-0.414
-Seasonal Naive,TiRex,0.05,0.01,0.1,-0.505,-0.623,-0.408
-Seasonal Naive,Toto-1.0,0.08,0.03,0.14,-0.46,-0.575,-0.362
-Seasonal Naive,TabPFN-TS,0.06,0.02,0.11,-0.501,-0.61,-0.409
-Seasonal Naive,Moirai-2.0,0.13,0.07,0.2,-0.442,-0.555,-0.347
-Seasonal Naive,Chronos-Bolt,0.09,0.04,0.15,-0.424,-0.527,-0.336
-Seasonal Naive,Sundial-Base,0.12,0.06,0.19,-0.375,-0.469,-0.29
-Seasonal Naive,Stat. Ensemble,0.165,0.1,0.23,-0.215,-0.283,-0.161
-Seasonal Naive,AutoARIMA,0.2,0.125,0.265,-0.153,-0.217,-0.099
-Seasonal Naive,AutoETS,0.315,0.225,0.415,-0.045,-0.147,0.057
-Seasonal Naive,AutoTheta,0.22,0.14,0.31,-0.16,-0.219,-0.11
-Seasonal Naive,Naive,0.525,0.435,0.61,0.058,-0.017,0.127
-Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0
-Seasonal Naive,Drift,0.64,0.53,0.73,0.079,-0.001,0.153
-Drift,Chronos-2,0.07,0.03,0.12,-0.792,-0.994,-0.62
-Drift,TimesFM-2.5,0.08,0.03,0.14,-0.639,-0.801,-0.507
-Drift,TiRex,0.09,0.04,0.15,-0.635,-0.796,-0.504
-Drift,Toto-1.0,0.1,0.04,0.17,-0.585,-0.735,-0.459
-Drift,TabPFN-TS,0.11,0.05,0.17,-0.63,-0.807,-0.488
-Drift,Moirai-2.0,0.13,0.07,0.2,-0.566,-0.715,-0.435
-Drift,Chronos-Bolt,0.1,0.05,0.16,-0.546,-0.695,-0.42
-Drift,Sundial-Base,0.19,0.12,0.27,-0.493,-0.66,-0.354
-Drift,Stat. Ensemble,0.14,0.08,0.21,-0.32,-0.415,-0.238
-Drift,AutoARIMA,0.25,0.17,0.35,-0.252,-0.353,-0.163
-Drift,AutoETS,0.26,0.18,0.34,-0.135,-0.24,-0.044
-Drift,AutoTheta,0.22,0.14,0.3,-0.26,-0.351,-0.188
-Drift,Naive,0.16,0.09,0.23,-0.023,-0.051,0.005
-Drift,Seasonal Naive,0.36,0.27,0.47,-0.086,-0.181,0.001
-Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/pairwise_WQL.csv

@@ -1,226 +0,0 @@
-model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
-Chronos-2,Chronos-2,0.5,0.5,0.5,0.0,0.0,0.0
-Chronos-2,TiRex,0.74,0.65,0.82,0.091,0.058,0.126
-Chronos-2,TimesFM-2.5,0.72,0.63,0.8,0.09,0.059,0.122
-Chronos-2,Toto-1.0,0.7,0.6,0.79,0.119,0.084,0.156
-Chronos-2,TabPFN-TS,0.81,0.73,0.89,0.105,0.072,0.134
-Chronos-2,Moirai-2.0,0.86,0.79,0.92,0.136,0.105,0.17
-Chronos-2,Chronos-Bolt,0.9,0.84,0.95,0.147,0.114,0.18
-Chronos-2,Sundial-Base,0.97,0.93,1.0,0.225,0.194,0.255
-Chronos-2,Stat. Ensemble,0.95,0.9,0.99,0.38,0.324,0.431
-Chronos-2,AutoARIMA,0.96,0.92,0.99,0.367,0.316,0.416
-Chronos-2,AutoETS,0.95,0.9,0.99,0.597,0.5,0.684
-Chronos-2,AutoTheta,0.98,0.95,1.0,0.474,0.418,0.529
-Chronos-2,Seasonal Naive,0.98,0.95,1.0,0.515,0.469,0.559
-Chronos-2,Naive,0.98,0.95,1.0,0.652,0.603,0.693
-Chronos-2,Drift,0.98,0.95,1.0,0.654,0.603,0.697
-TiRex,Chronos-2,0.26,0.18,0.35,-0.1,-0.144,-0.062
-TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
-TiRex,TimesFM-2.5,0.555,0.455,0.65,-0.001,-0.02,0.017
-TiRex,Toto-1.0,0.685,0.6,0.77,0.03,0.009,0.05
-TiRex,TabPFN-TS,0.64,0.54,0.74,0.016,-0.033,0.063
-TiRex,Moirai-2.0,0.795,0.715,0.87,0.05,0.03,0.073
-TiRex,Chronos-Bolt,0.815,0.735,0.885,0.061,0.039,0.083
-TiRex,Sundial-Base,0.895,0.835,0.95,0.148,0.11,0.185
-TiRex,Stat. Ensemble,0.89,0.83,0.95,0.318,0.262,0.374
-TiRex,AutoARIMA,0.93,0.87,0.98,0.304,0.256,0.352
-TiRex,AutoETS,0.92,0.86,0.97,0.558,0.449,0.655
-TiRex,AutoTheta,0.95,0.9,0.99,0.421,0.365,0.479
-TiRex,Seasonal Naive,0.99,0.96,1.0,0.467,0.42,0.515
-TiRex,Naive,0.99,0.96,1.0,0.617,0.565,0.663
-TiRex,Drift,0.97,0.93,1.0,0.619,0.567,0.666
-TimesFM-2.5,Chronos-2,0.28,0.2,0.37,-0.099,-0.139,-0.063
-TimesFM-2.5,TiRex,0.445,0.35,0.545,0.001,-0.017,0.019
-TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
-TimesFM-2.5,Toto-1.0,0.56,0.475,0.655,0.031,0.006,0.057
-TimesFM-2.5,TabPFN-TS,0.64,0.54,0.74,0.017,-0.031,0.062
-TimesFM-2.5,Moirai-2.0,0.73,0.645,0.81,0.051,0.028,0.076
-TimesFM-2.5,Chronos-Bolt,0.76,0.685,0.835,0.062,0.04,0.085
-TimesFM-2.5,Sundial-Base,0.905,0.845,0.96,0.148,0.114,0.183
-TimesFM-2.5,Stat. Ensemble,0.9,0.84,0.95,0.319,0.262,0.376
-TimesFM-2.5,AutoARIMA,0.93,0.88,0.98,0.304,0.255,0.357
-TimesFM-2.5,AutoETS,0.9,0.84,0.95,0.558,0.449,0.655
-TimesFM-2.5,AutoTheta,0.96,0.91,0.99,0.422,0.362,0.483
-TimesFM-2.5,Seasonal Naive,0.99,0.97,1.0,0.467,0.422,0.518
-TimesFM-2.5,Naive,0.99,0.97,1.0,0.617,0.564,0.664
-TimesFM-2.5,Drift,0.97,0.93,1.0,0.62,0.565,0.667
-Toto-1.0,Chronos-2,0.3,0.21,0.4,-0.134,-0.185,-0.092
-Toto-1.0,TiRex,0.315,0.23,0.4,-0.031,-0.053,-0.009
-Toto-1.0,TimesFM-2.5,0.44,0.345,0.525,-0.032,-0.061,-0.006
-Toto-1.0,Toto-1.0,0.5,0.5,0.5,0.0,0.0,0.0
-Toto-1.0,TabPFN-TS,0.54,0.45,0.63,-0.015,-0.073,0.036
-Toto-1.0,Moirai-2.0,0.59,0.49,0.68,0.02,-0.003,0.044
-Toto-1.0,Chronos-Bolt,0.61,0.52,0.695,0.032,0.003,0.061
-Toto-1.0,Sundial-Base,0.795,0.715,0.87,0.121,0.079,0.163
-Toto-1.0,Stat. Ensemble,0.81,0.73,0.88,0.297,0.235,0.356
-Toto-1.0,AutoARIMA,0.85,0.78,0.92,0.282,0.23,0.336
-Toto-1.0,AutoETS,0.84,0.76,0.91,0.545,0.433,0.645
-Toto-1.0,AutoTheta,0.93,0.87,0.97,0.403,0.341,0.465
-Toto-1.0,Seasonal Naive,0.96,0.92,0.99,0.45,0.397,0.501
-Toto-1.0,Naive,0.97,0.93,1.0,0.605,0.55,0.652
-Toto-1.0,Drift,0.97,0.93,1.0,0.607,0.551,0.656
-TabPFN-TS,Chronos-2,0.19,0.11,0.27,-0.117,-0.155,-0.078
-TabPFN-TS,TiRex,0.36,0.26,0.46,-0.016,-0.067,0.032
-TabPFN-TS,TimesFM-2.5,0.36,0.26,0.46,-0.017,-0.066,0.03
-TabPFN-TS,Toto-1.0,0.46,0.37,0.55,0.015,-0.037,0.068
-TabPFN-TS,TabPFN-TS,0.5,0.5,0.5,0.0,0.0,0.0
-TabPFN-TS,Moirai-2.0,0.55,0.46,0.65,0.035,-0.017,0.084
-TabPFN-TS,Chronos-Bolt,0.52,0.43,0.61,0.046,0.0,0.092
-TabPFN-TS,Sundial-Base,0.79,0.71,0.87,0.134,0.09,0.175
-TabPFN-TS,Stat. Ensemble,0.795,0.715,0.875,0.307,0.247,0.365
-TabPFN-TS,AutoARIMA,0.83,0.75,0.9,0.293,0.239,0.348
-TabPFN-TS,AutoETS,0.85,0.78,0.92,0.554,0.442,0.654
-TabPFN-TS,AutoTheta,0.94,0.89,0.98,0.412,0.352,0.471
-TabPFN-TS,Seasonal Naive,0.97,0.935,0.995,0.458,0.412,0.506
-TabPFN-TS,Naive,0.97,0.93,1.0,0.611,0.556,0.657
-TabPFN-TS,Drift,0.95,0.9,0.98,0.613,0.557,0.661
-Moirai-2.0,Chronos-2,0.14,0.08,0.21,-0.158,-0.204,-0.118
-Moirai-2.0,TiRex,0.205,0.13,0.285,-0.053,-0.078,-0.031
-Moirai-2.0,TimesFM-2.5,0.27,0.19,0.355,-0.054,-0.082,-0.029
-Moirai-2.0,Toto-1.0,0.41,0.32,0.51,-0.021,-0.046,0.003
-Moirai-2.0,TabPFN-TS,0.45,0.35,0.54,-0.036,-0.091,0.017
-Moirai-2.0,Moirai-2.0,0.5,0.5,0.5,0.0,0.0,0.0
-Moirai-2.0,Chronos-Bolt,0.59,0.505,0.67,0.012,-0.013,0.037
-Moirai-2.0,Sundial-Base,0.865,0.79,0.93,0.103,0.062,0.14
-Moirai-2.0,Stat. Ensemble,0.84,0.76,0.91,0.282,0.222,0.343
-Moirai-2.0,AutoARIMA,0.89,0.82,0.95,0.267,0.214,0.322
-Moirai-2.0,AutoETS,0.87,0.8,0.93,0.537,0.421,0.639
-Moirai-2.0,AutoTheta,0.91,0.85,0.96,0.391,0.33,0.454
-Moirai-2.0,Seasonal Naive,0.93,0.87,0.97,0.439,0.387,0.49
-Moirai-2.0,Naive,0.95,0.9,0.99,0.597,0.541,0.645
-Moirai-2.0,Drift,0.95,0.9,0.99,0.599,0.542,0.649
-Chronos-Bolt,Chronos-2,0.1,0.05,0.16,-0.172,-0.22,-0.129
-Chronos-Bolt,TiRex,0.185,0.115,0.265,-0.065,-0.091,-0.041
-Chronos-Bolt,TimesFM-2.5,0.24,0.165,0.315,-0.066,-0.092,-0.042
-Chronos-Bolt,Toto-1.0,0.39,0.305,0.48,-0.033,-0.065,-0.003
-Chronos-Bolt,TabPFN-TS,0.48,0.39,0.57,-0.049,-0.101,-0.0
-Chronos-Bolt,Moirai-2.0,0.41,0.33,0.495,-0.012,-0.038,0.013
-Chronos-Bolt,Chronos-Bolt,0.5,0.5,0.5,0.0,0.0,0.0
-Chronos-Bolt,Sundial-Base,0.785,0.705,0.865,0.092,0.051,0.128
-Chronos-Bolt,Stat. Ensemble,0.85,0.78,0.92,0.274,0.215,0.332
-Chronos-Bolt,AutoARIMA,0.88,0.81,0.94,0.258,0.207,0.31
-Chronos-Bolt,AutoETS,0.87,0.8,0.93,0.531,0.418,0.635
-Chronos-Bolt,AutoTheta,0.96,0.92,0.99,0.384,0.323,0.446
-Chronos-Bolt,Seasonal Naive,0.97,0.94,1.0,0.432,0.384,0.482
-Chronos-Bolt,Naive,0.98,0.95,1.0,0.592,0.537,0.639
-Chronos-Bolt,Drift,0.98,0.95,1.0,0.594,0.539,0.643
-Sundial-Base,Chronos-2,0.03,0.0,0.07,-0.29,-0.343,-0.24
-Sundial-Base,TiRex,0.105,0.05,0.165,-0.173,-0.226,-0.124
-Sundial-Base,TimesFM-2.5,0.095,0.04,0.155,-0.174,-0.223,-0.129
-Sundial-Base,Toto-1.0,0.205,0.13,0.285,-0.138,-0.195,-0.085
-Sundial-Base,TabPFN-TS,0.21,0.13,0.29,-0.155,-0.212,-0.099
-Sundial-Base,Moirai-2.0,0.135,0.07,0.21,-0.114,-0.163,-0.066
-Sundial-Base,Chronos-Bolt,0.215,0.135,0.295,-0.101,-0.147,-0.053
-Sundial-Base,Sundial-Base,0.5,0.5,0.5,0.0,0.0,0.0
-Sundial-Base,Stat. Ensemble,0.68,0.58,0.76,0.2,0.124,0.268
-Sundial-Base,AutoARIMA,0.73,0.63,0.81,0.183,0.114,0.245
-Sundial-Base,AutoETS,0.71,0.61,0.79,0.486,0.353,0.603
-Sundial-Base,AutoTheta,0.77,0.68,0.85,0.321,0.25,0.391
-Sundial-Base,Seasonal Naive,0.91,0.85,0.96,0.374,0.322,0.43
-Sundial-Base,Naive,0.91,0.85,0.96,0.55,0.489,0.605
-Sundial-Base,Drift,0.88,0.81,0.94,0.553,0.487,0.611
-Stat. Ensemble,Chronos-2,0.05,0.01,0.1,-0.613,-0.757,-0.479
-Stat. Ensemble,TiRex,0.11,0.05,0.17,-0.466,-0.598,-0.355
-Stat. Ensemble,TimesFM-2.5,0.1,0.05,0.16,-0.468,-0.603,-0.355
-Stat. Ensemble,Toto-1.0,0.19,0.12,0.27,-0.422,-0.552,-0.307
-Stat. Ensemble,TabPFN-TS,0.205,0.125,0.285,-0.443,-0.575,-0.329
-Stat. Ensemble,Moirai-2.0,0.16,0.09,0.24,-0.393,-0.521,-0.285
-Stat. Ensemble,Chronos-Bolt,0.15,0.08,0.22,-0.377,-0.496,-0.274
-Stat. Ensemble,Sundial-Base,0.32,0.24,0.42,-0.25,-0.366,-0.142
-Stat. Ensemble,Stat. Ensemble,0.5,0.5,0.5,0.0,0.0,0.0
-Stat. Ensemble,AutoARIMA,0.5,0.41,0.6,-0.021,-0.061,0.02
-Stat. Ensemble,AutoETS,0.755,0.67,0.835,0.38,0.234,0.517
-Stat. Ensemble,AutoTheta,0.87,0.81,0.93,0.151,0.111,0.194
-Stat. Ensemble,Seasonal Naive,0.835,0.77,0.895,0.218,0.166,0.273
-Stat. Ensemble,Naive,0.97,0.93,1.0,0.438,0.386,0.484
-Stat. Ensemble,Drift,0.97,0.93,1.0,0.442,0.387,0.49
-AutoARIMA,Chronos-2,0.04,0.01,0.08,-0.58,-0.711,-0.461
-AutoARIMA,TiRex,0.07,0.02,0.13,-0.436,-0.543,-0.345
-AutoARIMA,TimesFM-2.5,0.07,0.02,0.12,-0.438,-0.556,-0.342
-AutoARIMA,Toto-1.0,0.15,0.08,0.22,-0.393,-0.505,-0.299
-AutoARIMA,TabPFN-TS,0.17,0.1,0.25,-0.414,-0.533,-0.315
-AutoARIMA,Moirai-2.0,0.11,0.05,0.18,-0.365,-0.476,-0.272
-AutoARIMA,Chronos-Bolt,0.12,0.06,0.19,-0.348,-0.449,-0.261
-AutoARIMA,Sundial-Base,0.27,0.19,0.37,-0.224,-0.324,-0.129
-AutoARIMA,Stat. Ensemble,0.5,0.4,0.59,0.021,-0.021,0.058
-AutoARIMA,AutoARIMA,0.5,0.5,0.5,0.0,0.0,0.0
-AutoARIMA,AutoETS,0.685,0.59,0.775,0.381,0.235,0.518
-AutoARIMA,AutoTheta,0.75,0.67,0.83,0.169,0.119,0.218
-AutoARIMA,Seasonal Naive,0.88,0.825,0.93,0.234,0.184,0.287
-AutoARIMA,Naive,0.91,0.85,0.96,0.449,0.389,0.499
-AutoARIMA,Drift,0.89,0.82,0.95,0.453,0.393,0.505
-AutoETS,Chronos-2,0.05,0.01,0.1,-1.48,-2.169,-0.999
-AutoETS,TiRex,0.08,0.03,0.14,-1.261,-1.901,-0.816
-AutoETS,TimesFM-2.5,0.1,0.05,0.16,-1.261,-1.898,-0.814
-AutoETS,Toto-1.0,0.16,0.09,0.24,-1.198,-1.815,-0.764
-AutoETS,TabPFN-TS,0.15,0.08,0.22,-1.242,-1.889,-0.793
-AutoETS,Moirai-2.0,0.13,0.07,0.2,-1.158,-1.773,-0.727
-AutoETS,Chronos-Bolt,0.13,0.07,0.2,-1.133,-1.742,-0.717
-AutoETS,Sundial-Base,0.29,0.21,0.39,-0.945,-1.517,-0.545
-AutoETS,Stat. Ensemble,0.245,0.165,0.33,-0.612,-1.072,-0.305
-AutoETS,AutoARIMA,0.315,0.225,0.41,-0.616,-1.074,-0.308
-AutoETS,AutoETS,0.5,0.5,0.5,0.0,0.0,0.0
-AutoETS,AutoTheta,0.57,0.47,0.67,-0.384,-0.762,-0.111
-AutoETS,Seasonal Naive,0.625,0.535,0.715,-0.27,-0.651,-0.012
-AutoETS,Naive,0.76,0.68,0.84,0.059,-0.228,0.255
-AutoETS,Drift,0.79,0.71,0.87,0.062,-0.223,0.256
-AutoTheta,Chronos-2,0.02,0.0,0.05,-0.901,-1.124,-0.718
-AutoTheta,TiRex,0.05,0.01,0.1,-0.728,-0.919,-0.575
-AutoTheta,TimesFM-2.5,0.04,0.01,0.09,-0.73,-0.934,-0.567
-AutoTheta,Toto-1.0,0.07,0.03,0.13,-0.676,-0.87,-0.517
-AutoTheta,TabPFN-TS,0.06,0.02,0.11,-0.701,-0.889,-0.542
-AutoTheta,Moirai-2.0,0.09,0.04,0.15,-0.642,-0.83,-0.491
-AutoTheta,Chronos-Bolt,0.04,0.01,0.08,-0.622,-0.804,-0.477
-AutoTheta,Sundial-Base,0.23,0.15,0.32,-0.473,-0.641,-0.333
-AutoTheta,Stat. Ensemble,0.13,0.07,0.19,-0.178,-0.24,-0.124
-AutoTheta,AutoARIMA,0.25,0.17,0.33,-0.203,-0.279,-0.135
-AutoTheta,AutoETS,0.43,0.33,0.53,0.278,0.1,0.433
-AutoTheta,AutoTheta,0.5,0.5,0.5,0.0,0.0,0.0
-AutoTheta,Seasonal Naive,0.69,0.6,0.78,0.078,0.007,0.148
-AutoTheta,Naive,0.85,0.78,0.92,0.338,0.277,0.392
-AutoTheta,Drift,0.79,0.71,0.87,0.342,0.277,0.399
-Seasonal Naive,Chronos-2,0.02,0.0,0.05,-1.063,-1.27,-0.883
-Seasonal Naive,TiRex,0.01,0.0,0.04,-0.875,-1.061,-0.724
-Seasonal Naive,TimesFM-2.5,0.01,0.0,0.03,-0.877,-1.074,-0.729
-Seasonal Naive,Toto-1.0,0.04,0.01,0.08,-0.818,-1.003,-0.658
-Seasonal Naive,TabPFN-TS,0.03,0.005,0.065,-0.846,-1.025,-0.7
-Seasonal Naive,Moirai-2.0,0.07,0.03,0.13,-0.781,-0.961,-0.632
-Seasonal Naive,Chronos-Bolt,0.03,0.0,0.06,-0.76,-0.929,-0.623
-Seasonal Naive,Sundial-Base,0.09,0.04,0.15,-0.598,-0.753,-0.474
-Seasonal Naive,Stat. Ensemble,0.165,0.105,0.23,-0.279,-0.376,-0.198
-Seasonal Naive,AutoARIMA,0.12,0.07,0.175,-0.306,-0.403,-0.225
-Seasonal Naive,AutoETS,0.375,0.285,0.465,0.213,0.012,0.394
-Seasonal Naive,AutoTheta,0.31,0.22,0.4,-0.085,-0.174,-0.008
-Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0
-Seasonal Naive,Naive,0.685,0.61,0.755,0.281,0.208,0.351
-Seasonal Naive,Drift,0.77,0.69,0.86,0.286,0.205,0.362
-Naive,Chronos-2,0.02,0.0,0.05,-1.87,-2.255,-1.52
-Naive,TiRex,0.01,0.0,0.04,-1.608,-1.963,-1.301
-Naive,TimesFM-2.5,0.01,0.0,0.03,-1.611,-1.972,-1.293
-Naive,Toto-1.0,0.03,0.0,0.07,-1.53,-1.877,-1.224
-Naive,TabPFN-TS,0.03,0.0,0.07,-1.568,-1.912,-1.254
-Naive,Moirai-2.0,0.05,0.01,0.1,-1.478,-1.816,-1.178
-Naive,Chronos-Bolt,0.02,0.0,0.05,-1.449,-1.767,-1.16
-Naive,Sundial-Base,0.09,0.04,0.15,-1.224,-1.535,-0.956
-Naive,Stat. Ensemble,0.03,0.0,0.07,-0.779,-0.938,-0.629
-Naive,AutoARIMA,0.09,0.04,0.15,-0.816,-0.997,-0.636
-Naive,AutoETS,0.24,0.16,0.32,-0.063,-0.343,0.186
-Naive,AutoTheta,0.15,0.08,0.22,-0.51,-0.644,-0.383
-Naive,Seasonal Naive,0.315,0.245,0.39,-0.391,-0.54,-0.263
-Naive,Naive,0.5,0.5,0.5,0.0,0.0,0.0
-Naive,Drift,0.84,0.77,0.91,0.007,-0.018,0.028
-Drift,Chronos-2,0.02,0.0,0.05,-1.889,-2.297,-1.517
-Drift,TiRex,0.03,0.0,0.07,-1.626,-1.992,-1.31
-Drift,TimesFM-2.5,0.03,0.0,0.07,-1.629,-2.005,-1.301
-Drift,Toto-1.0,0.03,0.0,0.07,-1.547,-1.905,-1.228
-Drift,TabPFN-TS,0.05,0.02,0.1,-1.585,-1.953,-1.257
-Drift,Moirai-2.0,0.05,0.01,0.1,-1.495,-1.849,-1.182
-Drift,Chronos-Bolt,0.02,0.0,0.05,-1.465,-1.797,-1.171
-Drift,Sundial-Base,0.12,0.06,0.19,-1.239,-1.572,-0.95
-Drift,Stat. Ensemble,0.03,0.0,0.07,-0.791,-0.961,-0.632
-Drift,AutoARIMA,0.11,0.05,0.18,-0.828,-1.021,-0.649
-Drift,AutoETS,0.21,0.13,0.29,-0.066,-0.344,0.183
-Drift,AutoTheta,0.21,0.13,0.29,-0.52,-0.664,-0.384
-Drift,Seasonal Naive,0.23,0.14,0.31,-0.401,-0.567,-0.258
-Drift,Naive,0.16,0.09,0.23,-0.007,-0.029,0.018
-Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/pivot_MASE.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TiRex,TimesFM-2.5,Toto-1.0,Moirai-2.0,Chronos-Bolt,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoTheta,AutoETS,Seasonal Naive,Naive,Drift
-ETT_15T,0.69485799,0.7187969186000001,0.7295309932,0.7577736165000001,0.7033446670000001,0.7033446670000001,0.7625422865,0.7138930444,0.9168897854,0.9168897854,0.8021815222,1.4293457361000002,0.9168897854,1.3670854222,1.4150881085
-ETT_1D,1.3413765239,1.3268087247,1.3611067371,1.3679488602,1.3461045465,1.3461045465,1.4991782178,1.4174248495,1.4122431037,1.4311893424,1.4485203425,1.437554375,1.4915469632,1.4615227122,1.493070532
-ETT_1H,1.1259593877,1.1177931379,1.1239074995,1.1128977698,1.126722477,1.126722477,1.1773747941,1.1439289261,1.2518551239,1.2616150196,1.284690664,1.6020946418,1.3227159047,1.7184142013,1.7823323561
-ETT_1W,2.6985269137,2.6203818352,2.6215325512,2.6388480518,2.6260535479,2.6260535479,2.8095121652,2.7153947044,2.6874358146,2.7471493707,2.8368188855,2.6393182441,2.6201185499,2.6201185499,2.7818783534
-LOOP_SEATTLE_1D,0.9599506681,0.9691232143,0.9509504834,1.0267654131,0.9859320366,0.9859320366,0.9622221749,0.9847669395,0.9983198527,0.9888675362,1.0222727765,1.0016712996,1.1753801207,2.1046903302,2.373789824
-LOOP_SEATTLE_1H,0.7939256103000001,0.8196171978,0.7539313166,0.8716561378000001,0.9188719391,0.9188719391,0.8527195266,0.9012445092,1.3516779911,1.3516779911,1.1797622806,1.6371768899,1.3516779911,1.7163837446,1.9650972819
-LOOP_SEATTLE_5T,0.6812013579,0.6919744542,0.7344527308000001,0.6999688034,0.8058940043,0.8058940043,0.7953950095000001,0.7671902281,0.9579580055,0.9670797236,0.9937342132,0.9723147042,0.8255932621000001,1.0149350727,1.1722532487
-M_DENSE_1D,0.772147294,0.9065688429,0.8575514804000001,1.0278691722,0.9249126529,0.9249126529,0.9194900606,0.8999893558000001,1.050932514,1.168959753,1.0771913138,1.0868806507,1.3500254954,1.72924802,1.7668414689
-M_DENSE_1H,0.7132003866000001,0.7118568012000001,0.6693472657,0.7547859889,0.7173406545000001,0.7173406545000001,0.7953014020000001,0.775167995,1.1917704339,1.1833753219,1.4265921364,1.4834949349,1.2398526574,2.9336598204,3.2462087048
-SZ_TAXI_15T,0.4999875771,0.5028743392,0.5037458638,0.5100538324,0.5100343925,0.5100343925,0.5231892934,0.5133618160000001,0.6992652752,0.6992652752,0.5704736672,0.6812952229,0.6992652752,0.7537395688,0.7757872137
-SZ_TAXI_1H,0.4878765553,0.4985809825,0.5174781061,0.4938355877,0.5054399823,0.5054399823,0.5369530543000001,0.6129421994,0.5582147152,0.6365009349,0.7816119684,1.6334537315,0.6288121204,0.7531606604000001,0.8861734094
-australian_tourism,0.8548583034,0.9757973332,0.9190089649,1.1349816052,1.1608053998,1.167418807,0.8868052992000001,0.9146409764,0.9323052664,1.0124394981,1.0449349521,0.9667110204,1.0994521146,1.5829350567,1.7960279922
-bizitobs_l2c_1H,0.3841207893,0.4680641032,0.4101674728,0.4671656853,0.4308821616,0.4308821616,0.4458486692,0.4647078621,0.7567537453000001,0.7748607972,0.7957254005000001,0.8231641016,1.0658712657,0.7707155721000001,0.7758078113
-bizitobs_l2c_5T,0.5169198847,0.7958700059,0.5787129625,0.7135649119,0.8001548681,0.8001548681,0.6193328173,0.4807818165,0.8068916121,0.9163295742,0.8740253941,0.8137418066000001,1.0666676717,0.7577504543,0.9386117981
-boomlet_1062,0.6776941572,0.6804959751,0.6916399154,0.669096311,0.7155932877000001,0.7110111948000001,0.6864002083,0.7574670104,1.0019575039,0.9510023935,1.0704423896,1.0502749806,0.9949683457,1.307870931,1.422512019
-boomlet_1209,0.798770705,0.8520639537,0.8179153246,0.7440166652,0.8596275988000001,0.8460080897000001,1.107972646,0.8887982072,1.0454166675,1.0378800323,1.1056064071,1.1827230135,1.1827230135,1.0545734336,1.2621289177
-boomlet_1225,0.232250887,0.2338512692,0.2365234795,0.2291020931,0.2436207845,0.2540221924,0.2509252394,0.2531870103,0.2904167694,0.2751876371,0.3003114491,0.3006939878,0.3736491907,0.3736491907,0.38068424
-boomlet_1230,1.3704917337,1.3783150604,1.3669883226,1.2932815419,1.4190498379,1.347504774,1.6398564855000002,1.4532188179,1.5513275237,1.5864985932,1.5854205097,1.7204862029,1.8986209303,1.563197,1.7478394522
-boomlet_1282,0.5228616857,0.4966357089,0.4938256811,0.4982816626,0.5229602404,0.5646901456,0.506661869,0.5169242862,0.6397695821,0.5924898465,0.6909553164000001,0.6889630667000001,0.8299194032,0.8299194032,0.8508297266
-boomlet_1487,0.5106600333,0.5187314221,0.5025569448,0.4821270726,0.5276634634,0.5268262285,0.6139593947,0.5573472172,0.6620511075000001,0.6561515279,0.6861485358,0.6618622173,0.7188988334,0.7369858245,0.8583652247
-boomlet_1631,0.7130379866000001,0.7478094028000001,0.7223220370000001,0.7231858750000001,0.7328154067,0.7407561889000001,0.8463187427000001,0.7619771372,0.9935726652,0.9935726652,0.7886354003,0.7850787279,0.9935726652,0.8639891085,0.8923058214
-boomlet_1676,0.7074172691,0.7093011606,0.6981823962,0.6906502502,0.7128114239000001,0.7192465308,0.958687274,0.7138061614,0.952905272,0.952905272,0.7772233453,0.7609898935,0.952905272,0.8198719238000001,0.8566079879
-boomlet_1855,0.5381849677,0.5278191175,0.5502667915,0.5280890902,0.543045447,0.5502545471,0.6402148315,0.59756436,0.7137233958,0.7334421170000001,0.7479719187,0.7732640618000001,0.8339307546,0.680593968,0.6861913673000001
-boomlet_1975,0.160743225,0.2344681763,0.2045403051,0.149489781,0.2722386223,0.2157351982,0.2447173505,0.2358591523,0.5885200818,0.6455594326,0.5697333157000001,0.6992926357,0.9041727794,0.6991117215,0.7004386998000001
-boomlet_2187,0.8118519892,0.8085306304000001,0.8936367348,0.881456274,0.9069913111,0.878419842,1.0030305987,0.9507442206,1.18136692,1.2812206603,1.2716040444,1.2537471952,1.3439772563,1.15749623,1.1700732484
-boomlet_285,0.3587658722,0.4007113979,0.4216519838,0.3609691019,0.470509393,0.5250469024000001,0.3931546055,0.5605665968,0.7526380608000001,0.7883015466000001,0.7305756426000001,0.8132574193000001,1.2487633378,1.2487633378,1.2792135195
-boomlet_619,0.4234249096,0.4418338294,0.4363557101,0.403240616,0.4306276617,0.5940973904,0.4305322873,0.435636804,1.0046773699,0.7170247042,1.0200015813,1.0937660371,1.1235570145,1.1235570145,1.1484320675
-boomlet_772,0.331309463,0.3435360993,0.3429964064,0.3279337304,0.3651670055,0.3956163828,0.3608040826,0.3920487456,0.5801758985000001,0.5361147759,0.6163309557000001,0.6702469495000001,0.6290173275000001,0.6290173275000001,0.6468050268000001
-boomlet_963,0.8390323316,0.8276719248000001,0.8600331800000001,0.8253396814,0.8725943582000001,0.8956312939000001,0.8758112548,0.849173272,1.1173343797,1.1099255146,1.1004605986,1.2932431478,1.1186074012,1.1186074012,1.1367798246
-ecdc_ili,2.7551221041,2.8841261459,2.6316220318,3.0233475859,2.9286752636,3.1427261211,2.8110944773,3.0367688213,4.3217227901,4.1963805493,4.2528905756,4.5974303581,4.1680850656,4.1680850656,4.3553339847
-entsoe_15T,0.584613927,0.5986061889000001,0.5903166384,0.7502653602,0.5933312818,0.6061735307,0.6094490505,0.7577799507,0.9314587074,0.9314587074,0.7298063048,4.0070290526,0.9314587074,1.9045500461,2.0547959624
-entsoe_1H,0.5379602267,0.585380389,0.5848723966,0.5907812391,0.5924217639,0.556447549,0.5377189816,0.8117895189000001,1.1144887827,1.1176425426,1.1626008244,2.0475241128,1.1014887222,2.0553481854,2.1575810109
-entsoe_30T,0.5438155024,0.6645923404,0.6957880272,0.6254143824,0.5973972755,0.6326216104,0.6350493808000001,0.7796412031000001,1.0577908951,1.2299298482,1.0040370692,3.2738378374,1.2161814769,2.0733355971,2.0462297626
-epf_be,0.6470253095,0.6743990368,0.6101898021000001,0.7065052879,0.6709379406,0.7272945434,0.6701844387,0.7222255864,0.9813953164,1.0947820348,1.0193650689,1.3929299519,1.0270532112,1.3609414277,1.3744478654
-epf_de,0.6042262391000001,1.2971032281,1.2798889483,1.3369704527,1.2281783328,1.2655265893,0.5674762585,1.3227461317,1.3821017935,1.6231053542,1.407198376,1.741711499,1.7085955085,1.7417240476,1.7695511326000002
-epf_fr,0.4500648874,0.5040405462,0.4906901724,0.5253012985000001,0.5034595344,0.5638378752000001,0.4181703173,0.5243569292,0.7410301647,0.988839379,0.8447495724,0.9665291295,0.8501188428,1.160344512,1.1771067225
-epf_np,0.8501927354000001,1.22255132,1.4402381231,1.3589475358,1.1997562864,1.2409440197,0.8709423510000001,1.0947012554,1.514694987,1.7096191588,1.514020593,2.3244018164,1.7613036184,2.3154975383,2.3147871788
-epf_pjm,0.4718976596,0.5057793683,0.5310653587,0.5821871052,0.5630774342,0.5357283349,0.5339815846,0.5214143203,0.5294817721,0.5572886801,0.6832480189,0.9847286701,0.581167898,0.9849340527,0.9983941216
-ercot_1D,1.1382701552,1.0650322509,1.068637649,1.1712118632,1.2043414824,1.150459162,1.2627737925,1.1023893836,1.5207401049,1.3979692163,1.5801931826,1.5952697559,1.5987746508,1.5840542166,1.5904988169
-ercot_1H,1.3575339616,1.3770443905,1.4902437934,1.4168899965,1.3940551857,1.3914155571,1.5551131815,1.4647042938,1.610467295,1.4621684481,1.5835893744,3.196839145,1.5757631018,3.1089998641,3.1208481544
-ercot_1M,0.9503422118,1.0121701532,0.9788665964,1.2709588716,1.2434972767,0.9921791164,1.2288572299,1.0612308441,0.9720301101,0.9971198091,1.2051001185,0.9527701547,1.112833491,4.4343715733,4.8730234607
-ercot_1W,1.2194617137,1.2277888798,1.1887344162,1.3470027368,1.3362468506,1.2261966297,1.6253611467,1.4770568791,2.6623042168,2.6323348127,2.66207578,2.6788985082,2.6319098716,2.6319098716,2.6294908648
-favorita_stores_1D,1.1401981362,1.1934094375,1.1675502677,1.2803612723,1.2046882591,1.2688834676,1.1942878338,1.2200293401,1.3400112075,1.4187797295,1.3758675565,1.3798606395,1.7590444116,1.8830413748,1.8964809396
-favorita_stores_1M,1.9752000518,2.2147063817,2.2337935781,2.2864924589,2.3235561779,2.4178143615,2.1757603336,2.4060388473,2.1181450497,2.207495882,2.1335008012,2.1221172271,2.2823009488,1.9974006051,2.0640825589
-favorita_stores_1W,2.2987284037,2.4234607638,2.2900534378,2.5080339706,2.5772702404,2.4748929287,2.5267645799,2.561243423,2.433980351,2.580066785,2.4768022229,2.5164520445000003,2.5171133578,2.5171133578,2.55263811
-favorita_transactions_1D,0.8493704997,1.3492617128,1.1514968036,1.1514968036,1.1514968036,1.1514968036,1.6727335501,1.324712294,1.2657781225,1.7409399844,1.2511063806,1.2765051213,1.820047183,1.8618151915,1.8593809033
-favorita_transactions_1M,1.2531106859,1.3596174423,1.331599249,1.6656037195,1.6105440091,1.6365070781000002,1.4261630874,1.6184833791000002,1.3044406101,1.4642003185,1.4898451778,1.3424992981,1.5197338218,1.4299319146,1.6051275429
-favorita_transactions_1W,1.498824674,1.7464561746,1.7479484715,1.9303724027,1.6846164482,1.7479484715,2.436781004,2.0992640534,1.6323769125,1.7908498043,1.7230820362,1.6826500128,1.5401800863,1.5401800863,1.6333667243
-fred_md_2025/cee,4.1602413715,3.9913296592,5.4995170613,5.4995170613,5.4995170613,5.4995170613,4.6737821611,5.6396642877000005,4.1492831073,6.3656916747,4.7268175598,4.1316582891,11.870151993,6.7148852073,4.6902098373
-fred_md_2025/macro,6.7212229021,6.2436277327,6.8925263969,6.8925263969,6.8925263969,6.8925263969,7.7467550529,6.9855690276,6.4760151224,7.2903875617,6.7085954414,6.5541834396,11.2729488966,7.1693509539,6.6706675334
-fred_qd_2025/cee,2.7861573589,2.5007210568,2.5230363656,2.1472810882,2.7932979017,2.9552801855,2.9044098381000003,4.1237296063,2.2117006108,2.3066503643000003,2.5784767122,2.4969928698,5.7264703884,4.3666041366,2.5885780238
-fred_qd_2025/macro,4.2345765547,4.2532915346,4.2312964035,4.0310227379,4.3026427477,4.4204306869,5.2932799619,5.0800217147,4.2291679217,4.4837099549,4.4089305862,4.4466602526,6.2532543175,4.9115310592,4.3735327936
-gvar,0.7105251571,0.7074487020000001,0.7192074299,0.7017595921,0.7279795748,0.7294646076,0.9010948704,0.8613958999,0.6831004748,0.7032889301,0.6996326439,0.7021303869000001,0.9186013653,0.7430842013000001,0.6965206846
-hermes,0.7761148308,0.8309937727000001,0.7871785508,1.2023433786,0.8853610442000001,0.8579267331,0.9123318673,0.959480348,1.8121264962,1.5317415273,1.8477529328,1.9852238872,1.9945193434,1.9945193434,2.0472163243
-hierarchical_sales_1D,0.6889480354,0.6853563806,0.6882027162000001,0.6820968126,0.6860304896,0.6860304896,0.7080055758,0.7291028689,0.8130155521,0.7735716427,0.8693462262,0.8639978187,0.9949475154,1.0417563414,1.0525564501
-hierarchical_sales_1W,0.7539268452,0.756678049,0.7507699991,0.7789897858,0.7733116574000001,0.7733116574000001,0.7740136921,0.8055524179,0.9004451123,0.9125112599,0.9154643589,1.0341883947,1.1617080217,1.1617080217,1.1894416421
-hospital,0.8734575253,0.8749408193,0.8629412822,0.9208820395,0.8888399515000001,0.8888399515000001,0.8808779374,0.9575868855,0.8766229998,0.9191221959,0.9215507227,0.9080008209,1.0188066511,1.10504708,1.1736654928
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-hospital_admissions_1W,0.7508052537000001,0.7609831368000001,0.7544838609000001,0.7781410151,0.7643243630000001,0.7622844235,0.7534416615,0.7598582682,0.7551866274,0.7540567649000001,0.7779256247,0.7540854723,1.0436412698,1.0436412698,1.0831848605
-jena_weather_10T,0.4309090205,0.4789954609,0.4373143782,0.4522280598,0.4805248693,0.4805248693,0.516391649,0.4900412345,0.7300235109000001,0.7300235109000001,0.4933654141,0.6119521222000001,0.7300235109000001,0.52703557,0.5533241331000001
-jena_weather_1D,1.4035573001,1.366539178,1.3763415621,1.411132821,1.3722903668,1.3722903668,1.4445769356,1.4276462192,1.5642224637000002,1.5277037002,1.6406694179,1.8504210406,1.8965701168,1.6994317303000002,1.7748810582
-jena_weather_1H,0.4373910058,0.437909858,0.4392356659,0.4476285627,0.4542878163,0.4542878163,0.5111184304,0.4459427668,0.4663908322,0.5091821891,0.4846005807,0.567832109,0.7399001552000001,0.5374935394,0.544552084
-kdd_cup_2022_10T,0.5199209021,0.5903298173,0.5903298173,0.5903298173,0.5903298173,0.5903298173,0.6951920674000001,0.5903298173,0.8187009928000001,0.8187009928000001,0.8117980851000001,0.7837343985,0.8187009928000001,0.7815687018,0.8809057293
-kdd_cup_2022_1D,0.8993091790000001,0.8990205149,0.8934942013,0.8967645421,0.9125687218,0.9069594704,0.9150751748,0.9235145261,0.9701127541,0.9552154519,0.9764039863,0.9888169951,1.0844353298,1.0306171345,1.0552765612
-kdd_cup_2022_30T,0.5419431132,0.5339588215000001,0.6348935773000001,0.5309168604,0.5156770351000001,0.6672184767,0.6765431227,0.5884124519,0.7681145469,0.7706658108000001,0.8416018012,0.8428842456000001,0.8221839563000001,0.8375670849,0.8782613104
-m5_1D,0.8805311862,0.8753446971000001,0.8851788247,0.8851788247,0.8851788247,0.8851788247,1.2236286692,0.9677992163,1.2236286692,1.0630838856,1.0805686532,1.0632996272,1.2236286692,1.3575007837,1.3783870781
-m5_1M,1.1638225847,1.1629101784,1.1576052027,1.2421800194,1.1773037824,1.1852216988,1.1871283177,1.199580668,1.1819548335,1.2131357033,1.2589934317,1.2139006267,1.3266370526,1.2671440715,1.3915794965
-m5_1W,1.140035557,1.1476955468,1.1647431618,1.1448252405,1.1500678738,1.1647431618,1.1604987437,1.1330759513,1.151797309,1.1567620351,1.1697733846,1.1672033988,1.3382424884,1.3382424884,1.3824089509
-proenfo_gfc12,0.8120010722000001,1.1241553096,1.0781317629,1.0781317629,1.0781317629,1.0781317629,1.0338246822,0.9935522058,1.5463300248,1.3848285732,1.5236522096,2.8114372878,1.428177457,2.6291046656,2.7528132313
-proenfo_gfc14,0.5386865885000001,0.9119046182,0.9285112769,0.9285112769,0.9285112769,0.9285112769,0.6406314316,0.463580052,1.1044049767,1.1681324943,1.185656643,1.3185527521,1.1988543772,3.7072504144,3.965045322
-proenfo_gfc17,0.6103925032,1.1380901313,1.0977005202,1.0977005202,1.0977005202,1.0977005202,0.8553343104000001,0.5526319982,1.4215223964,1.3821477017,1.3687323787,2.4127647331,1.5845048132,2.9066491955,3.063129907
-redset_15T,0.932090844,1.0022286368,0.8687945356000001,0.9533624543,1.1189004499,1.4783946982,2.1719934434,1.3649343142,1.0323384841,1.0323384841,1.597597259,1.0323384841,1.0323384841,32.0340548113,33.693269119600004
-redset_1H,1.5030009123,1.4573336789,1.4842036779,1.4166961403,1.5362745519,2.6712489552000003,1.4385006498,1.5646420584,1.761972427,1.8532418262,2.0202444582,2.2287068299,1.6826194185,8.1555516024,8.1864715752
-redset_5T,0.7864618582,0.9453718328,0.8482881600000001,0.8563860838,0.9344199171,1.1699231408,0.8423602104,1.0328105291,2.1640102467,2.0719155469,2.3397807513,1.0410587245,1.0410587245,3.1480546918,3.498018604
-restaurant,0.8599089924000001,0.8522179843000001,0.8503096514,0.8858745474,0.8659689945,0.8659689945,0.8689264082,0.8630231098000001,0.8717386477,0.9400606666,0.9184892419,0.9391232286,1.1194990361,1.3993248163,1.4760866124
-rohlik_orders_1D,1.1869082034,1.2033753235,1.2504095981,1.3775879584,1.176067019,1.3016096527,1.547892006,1.3992895671,1.3812243729,1.5623634243,1.5185276042,1.4870315996,1.7782872152,2.4354371077,2.4953294972
-rohlik_orders_1W,1.5470873372,1.5828643626,1.6591862082,1.7980310562,1.8740325635,1.7219242334,1.9886917666,2.0951936703,1.71276995,1.7324467216,1.7121743288,1.7583263983,1.7311823321,1.7311823321,1.7566346002
-rohlik_sales_1D,1.1015615434,1.3844809879,1.3237942192,1.4539084863,1.4020532381,1.3870630122,1.6150435602000002,1.3442522277,1.4759756392,1.5092369137,1.4939231302,1.4985533338,1.6150435602000002,1.7485285579,1.7665358583000002
-rohlik_sales_1W,1.5596721638000002,1.7354311654,1.6890705942,1.8031174582,1.8214908337,1.8466635227,1.5204800642,1.8996288065,1.8249729553,2.03785374,1.8363584706,1.8899729917,1.9154827568,1.9154827568,1.990816422
-rossmann_1D,0.3557646152,0.6600669122,0.6105528999000001,0.6814055866000001,0.6480208978,0.6371181918000001,0.2944517637,0.6154624197,0.6678317895,0.6544020781000001,0.7447792464,0.6912520753,0.7885548119,1.6195142378,1.6307564391
-rossmann_1W,0.3711869945,0.6218198341,0.654277349,0.6318509753,0.6439797931,0.6451761650000001,0.3046311863,0.6790382249,0.6512769978,0.6688824183000001,0.6643635235,0.6684782378,0.7960291356,0.7960291356,0.8779634547
-solar_1D,0.7564704595,0.7780027014,0.7870867805,0.7929503108,0.8134821640000001,0.8068081935,0.7848225151,0.7866198335,0.7950641496,0.8072395918,0.8376174094000001,0.8017773736,0.9900696291,0.9666968847,1.1102918555
-solar_1W,1.1789594159,1.5031298228,1.4613966872000002,1.798309645,2.0395669896,1.2719881759,1.0931583831,1.1001413936,1.7922537528,1.7335090246,2.0936290706,1.6267514936,1.9187253475,1.9187253475,2.5896105932
-solar_with_weather_15T,0.8640997518,1.0486903807,1.1152651553,0.9403149212,1.0763836253,0.9765226278,0.9444749979,1.1181879294,1.0500997059,1.0500997059,1.2728696954,2.4161932928,1.0500997059,1.989464318,2.2100824084
-solar_with_weather_1H,1.020146174,1.1582715132,1.0512633641,1.0585275477,1.1358492836,1.0720382243,0.8628512004000001,1.3031564924,1.3147978753,1.1133137476,1.5354303629,2.2682380188,1.0619359164,2.2682071361,2.2668539311
-uci_air_quality_1D,1.3132142922,1.4312317693,1.5147124464,1.5881022678,1.4417059353,1.3886570934,1.5308266811,1.3990875663,1.3836855571,1.5585347706,1.4428092347,1.3629041949,1.7337243007,2.1090746157,2.2085178759
-uci_air_quality_1H,1.0205968913,1.1061991604,1.1225931257,1.1111893485,1.196474258,1.1161574722,1.1751368971,1.1924183278,1.2991890754,1.3697649929,1.3457656206,10.6979097385,1.4424742241,1.681575288,1.7613798983
-uk_covid_nation_1D/cumulative,9.4467008809,8.929376688,7.9247162242,7.7416129012,8.1949951543,10.4547608004,16.8706126207,18.3255496813,8.8341213691,9.559789598,20.1075262656,8.1052795609,30.7671471585,25.4400086758,18.9781830611
-uk_covid_nation_1D/new,2.5004449077,2.3835910907,2.5834199323,2.4705193977,2.5489344845,2.5913882505,2.5101268505,2.5802902251,3.1171908684,4.1917555777,2.8839990291,3.0620508133,2.8996526561,2.9050530888,2.9572817188
-uk_covid_nation_1W/cumulative,3.5475301481,3.609263087,5.0226305819,3.6488116454,3.6495679007,4.3362420429,3.1721656359,5.8508558031,2.5352192342,10.5120021053,5.7016692031,2.8531029011,8.7736590065,8.7736590065,5.5370707385
-uk_covid_nation_1W/new,6.281728984,5.4013881506,4.6024864277,6.3940786735,4.5611907188,5.1432817198,4.8064190531,4.2487016855,6.252228662,13.5408520302,5.813977235,6.0384476605,5.8035952813,5.8035952813,6.0322653616
-uk_covid_utla_1D/new,4.545983792,4.4705040881,4.1803353814,4.8383241752,4.2104220413,4.2680470788,4.582072345,4.1215258287,6.2345012475,8.1828726428,5.820038846,6.304086527,5.2400849649,5.9561884523,6.0662829111
-uk_covid_utla_1W/cumulative,19.8762833001,21.7652806241,21.5163025658,18.8192829039,21.8269429168,20.0769214614,19.7677867998,27.7918750277,15.8503479662,15.7434113202,21.4127050868,17.1124689005,26.0772211706,26.0772211706,20.9787577016
-us_consumption_1M,1.7363780834,1.7646639097,1.931250606,1.8634380437,1.7680304079,1.8462616555,1.8857443126,2.1034739353,1.7231034056,1.8263458048,1.9291138764,1.7093004134,3.1370276717,2.1549055801,1.9442203469
-us_consumption_1Q,2.1646931617,2.2808950609,2.3646254512,2.1108276882,2.2065057075,2.2129312479,3.7648783035,3.2110317266,2.2422219291,2.3510275878,2.6435650081,2.2553091192,3.8594112114,2.9276892853,2.513273914
-us_consumption_1Y,4.5651597874,4.5542723191,4.8010821783,4.7990321771,5.8237789501,5.0426493832,5.0551837428,6.4807387204,4.453578755,4.2587442225,5.619694319,4.7574382247,7.3594854558,7.3594854558,5.51566537
-walmart,0.8167399666,0.8862297442,0.8614820536000001,1.1257617414,1.0559167299,0.9670980075,0.8317842871000001,0.9842192099,1.3563890452,1.2473824838,1.4178781632000002,1.7226801126,1.5240930953,1.5240930953,1.7491246911
-world_co2_emissions,3.2534412006,3.218938845,3.4342160899,3.2426033895,3.4946390667,3.3725385277,3.2702702323,4.0694291919,3.1762491991,3.3062345239,3.2294516112,3.2990698517,3.6978220012,3.6978220012,3.1983782167
-world_life_expectancy,1.4501893466,1.3385484305,1.4011006524,2.0742068807,2.147670042,1.6264414789,1.3478888107,1.7987773288,1.5403532831,1.4974247187,1.7124546782,1.5355990211,2.2537764593,2.2537764593,1.8420117568
-world_tourism,3.7800260471,3.8100716189,4.1100857021,3.7867597144,3.9098929656,3.9050210198,3.3270326033,4.7392839763,3.0020746256,3.0968725539,2.9312038042,3.4851749726,3.8281690603,3.8281690603,2.8785102461000003

tables/pivot_MASE_baseline_imputed.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TiRex,TimesFM-2.5,Toto-1.0,Moirai-2.0,Chronos-Bolt,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoTheta,AutoETS,Seasonal Naive,Naive,Drift
-ETT_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-ETT_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ETT_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ETT_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1H,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-LOOP_SEATTLE_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-SZ_TAXI_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-SZ_TAXI_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1209,False,False,False,False,False,False,False,False,False,False,False,True,False,False,False
-boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1282,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1631,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-boomlet_1676,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_619,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_10T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-jena_weather_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_10T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1D,False,False,False,False,False,False,True,False,True,False,False,False,False,False,False
-m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc12,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc14,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc17,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_15T,False,False,False,False,False,False,False,False,True,True,False,True,False,False,False
-redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_5T,False,False,False,False,False,False,False,False,False,False,False,True,False,False,False
-restaurant,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1D,False,False,False,False,False,False,True,False,False,False,False,False,False,False,False
-rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_MASE_leakage_imputed.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TiRex,TimesFM-2.5,Toto-1.0,Moirai-2.0,Chronos-Bolt,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoTheta,AutoETS,Seasonal Naive,Naive,Drift
-ETT_15T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-ETT_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-ETT_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-ETT_1W,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_5T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-SZ_TAXI_15T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-SZ_TAXI_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_5T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1209,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1282,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1631,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1676,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_619,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1D,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1W,False,False,True,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/cee,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/macro,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1W,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-hospital,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_10T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-jena_weather_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-jena_weather_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_10T,False,True,True,True,True,False,False,True,False,False,False,False,False,False,False
-kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1D,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1W,False,False,True,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc12,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc14,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc17,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-redset_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-restaurant,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_SQL.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TiRex,TimesFM-2.5,Toto-1.0,Moirai-2.0,Chronos-Bolt,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
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-ETT_1D,1.1315469453,1.1014613305,1.1440582667,1.1431991194,1.1321824157,1.1321824157,1.2302029516,1.2461046836,1.2707356751,1.2587593653,1.3559628802,1.3789083105,1.3728451389,1.4222525092,1.4528881358
-ETT_1H,0.8829666059,0.8735827998000001,0.8822637379,0.8726715063,0.9435863248,0.9435863248,0.93321436,0.9634114324,1.2717102201,1.052404926,1.7645010362,2.0612851578,1.2068899431,2.3136589326,2.4221862813
-ETT_1W,2.3200722486,2.264520334,2.2485701754,2.2807752001,2.2795034775,2.2795034775,2.4105571965,2.4688224531,2.4073690783,2.4423112635,2.39380306,2.603472326,2.5092678823,2.5092678823,2.63212947
-LOOP_SEATTLE_1D,0.7791980647000001,0.7923359463,0.7738014961,0.8309489631,0.8051325971000001,0.8051325971000001,0.7804864737,0.8625023325000001,0.8198461462000001,0.8099517857,0.8246251865,0.8528499579000001,1.0461703776,2.4710363002,2.6285047085
-LOOP_SEATTLE_1H,0.6390561982,0.6558214249000001,0.6206804936,0.6981449455000001,0.7646847805,0.7646847805,0.6785128884,0.7695770803,1.5008790861,1.5008790861,2.6385256214,2.8320370482,1.5008790861,3.5094786471,3.7005853436
-LOOP_SEATTLE_5T,0.5325339887,0.5488731010000001,0.5953126390000001,0.5612747662,0.7101241523,0.7101241523,0.6414697138000001,0.6606205367,1.0436727136,1.0012742792,1.1550368249,1.1098659427,0.7515879948,1.8612988657,2.0191661844
-M_DENSE_1D,0.6463243544,0.7460414464,0.7075343346,0.8417803746,0.7589658968,0.7589658968,0.7560067856,0.7844887054,0.9647884446,0.9701754768,1.0734765679,1.1434781616,1.262653406,2.1937270172,2.2311998044
-M_DENSE_1H,0.5854558868,0.5867504602,0.556059175,0.6211696495,0.5952543328000001,0.5952543328000001,0.6460899053,0.6604636921,1.1265341106,1.0625833478,59.0195299046,3.0980400553,1.3040659305,3.8689657918,4.0939855017
-SZ_TAXI_15T,0.3932968859,0.3955981846,0.3966454244,0.4014560725,0.413175328,0.413175328,0.4285012823,0.4438538338,0.5602190946,0.5602190946,2.3550253131,0.5126750232,0.5602190946,1.3787889942,1.4174386493
-SZ_TAXI_1H,0.3977234494,0.4050422075,0.4156030479,0.4183039495,0.4264135301,0.4264135301,0.4935099732,0.5170317067,0.6894973186000001,0.6584161139,12313683383.147636,0.983741696,0.8375139431,2.6856786961,2.9588624372
-australian_tourism,0.6769555543,0.7859715496,0.7323027992000001,0.8897084606000001,0.9175231952,0.9282641589,0.6989805801,0.7908486579,0.7300394505000001,0.8009680433,0.7615891772000001,0.8129699438,0.8947401082,1.5916364579,1.7922461374
-bizitobs_l2c_1H,0.3010456324,0.3662951077,0.3261346281,0.3700783044,0.3419873985,0.3419873985,0.3540742815,0.4121569345,0.6336059931,0.6185436377,0.7179372865,0.6824779096,0.8851906828,0.6654111557,0.669961201
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-boomlet_1209,0.6799100048000001,0.7293072077,0.7045922388,0.645080569,0.7562347037,0.7840808515000001,1.0159986311,0.7807326884,2.4692530935,1.1085996591,1.26373922,2.7292377595,1.26373922,3.2386174338,3.5173454757
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-boomlet_1230,1.2010320136,1.1859324313,1.1874247824,1.1375594979,1.2863247224,1.266108854,1.6130013764000002,1.3047326747,3.3900350214,1.8500253092,393376667139840.8,3.9779653374,2.0369976474,5.548152717,5.9878393324
-boomlet_1282,0.4211360536,0.4089033383,0.4034160537,0.4069384603,0.4269211119,0.461810646,0.4252624699,0.4521593639,0.7391125197,0.5565242655,0.9135627329,0.9716916511,1.5394068156,1.5394068156,1.5690882811
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-boomlet_1631,0.5718707143,0.5981611197000001,0.5790200299,0.5808311118,0.5907980989,0.6194052562,0.6970609561000001,0.6443979368,0.8513712543,0.8513712543,0.7214099148,0.7282739185,0.8513712543,1.3493380035,1.3890549561
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-boomlet_1855,0.4615736869,0.4500166174,0.4725778373,0.4524286557,0.464879974,0.4695926826,0.6233137320000001,0.5254199296000001,1.1233499934,1.0829482214,1.1848850765,1.204414213,1.4642665486,3.2734556686,3.2968156747
-boomlet_1975,0.1333320445,0.1921381366,0.1671766157,0.1262384828,0.2195469335,0.1793929044,0.2069952508,0.2023056594,0.5478588809,0.5505765392,0.6113593807000001,0.6292227757000001,0.8427719263000001,0.6114911297,0.6131317081000001
-boomlet_2187,0.7122729749000001,0.7105001322000001,0.8020040101,0.7637500168,0.8067927027,0.7746853019000001,0.9340158386,0.8515537165,1.2729717642,1.3028566624,1.3069851719,1.389411779,1.6696910974,3.0072867381,3.0279388411
-boomlet_285,0.290064067,0.3453755039,0.3965655545,0.3185360094,0.4274151922,0.476702899,0.7127008195,0.4879211264,1.2619335645,1.1887776805,1.2033358677,1.3318503729,6.0221504695,6.0221504695,6.1302545159
-boomlet_619,0.3232653941,0.3410809257,0.3398320588,0.309878085,0.3294344187,0.4708614915,0.3305066441,0.3705071077,0.7771722948,0.5544648012,0.8944230183,0.8352158487,1.2751647052,1.2751647052,1.3005697258
-boomlet_772,0.2827797602,0.2962086426,0.2948668716,0.2810029794,0.3139115775,0.3391929948,0.3295303206,0.3520560133,1.1794168073,0.7834129974,531470228.16674185,1.4390517121,2.4909982911,2.4909982911,2.5366947218
-boomlet_963,0.7165952951,0.7184034841,0.7387586339000001,0.7199999526,0.7505200217,0.7786787266,0.7960581525,0.7521897883,1.3348061563,1.1056032467,1.6089011238,1.4536844039,1.6474289575,1.6474289575,1.6758761104
-ecdc_ili,2.271393724,2.4106397038000003,2.2150356629,2.554483793,2.4544933394,2.6531174908,2.3819418015,2.7054455064000003,3.8373720704,3.6414550365,4.0788004388,3.8437922843,3.7763740551,3.7763740551,3.9298378029
-entsoe_15T,0.4539692578,0.4692753774,0.4709170997,0.5909160927,0.478288762,0.5061680935,0.4837394321,0.6669136238000001,0.7806910892000001,0.7806910892000001,3.0289340615,0.5794334285,0.7806910892000001,1.5177144586,1.6350644474
-entsoe_1H,0.4292730733,0.4701221987,0.4680893711,0.4795713506,0.4870732428,0.4574194964,0.4419444303,0.7440774844,0.8919673675,0.8725110581000001,1.9050298297,0.9723015525,1.0561195152,1.91535268,2.0110168444
-entsoe_30T,0.4335548965,0.5229834486,0.5657877209000001,0.4958133037,0.4883587468,0.5294317223,0.5116608489,0.7215674609,0.8465224818,0.980742171,2.492761432,0.7996836131,1.0103240484,1.6091505549,1.5911802499
-epf_be,0.5032958894,0.5270142745,0.4937475347,0.5648275564,0.5281380165,0.573131006,0.5324134798,0.6465423658,1.2134724304,1.0560628345,1.5343230451,1.4843337008000002,1.1502802436,3.0844576332,3.1070840486
-epf_de,0.4911168264,1.0321659189,1.0300427797,1.1058453126,1.0163698014,1.0208059246,0.4402987265,1.1830726521,1.1665641061,1.2777053882,1.4013079444,1.4943830235,1.3876764191,1.4012218785,1.4206596948
-epf_fr,0.3617710942,0.4013687759,0.4091649941,0.4256540696,0.4092032808,0.4389128912,0.3307410445,0.461064889,1.1455989836,1.1580732451,0.8989034107,1.5911735884,1.2455233831,3.8189174071,3.8479452737
-epf_np,0.6581143676,0.9662474004,1.1706350574,1.0368665291,0.9253466826,0.971072301,0.6592724518,0.9450979115,1.2844205231,1.3932103761,1.9332061254,1.2812041765,1.5298484821,1.9403665636,1.9414483144
-epf_pjm,0.3815757302,0.4041710148,0.4263079971,0.4518928599,0.4405348981,0.4216993095,0.4270292068,0.4679144039,0.4870679588,0.48187052,0.9138786189,0.6031844165,0.5152721396000001,0.9298473491,0.9378548257
-ercot_1D,0.8691909797,0.8182837121000001,0.8296777262,0.8800154437000001,0.9470906371,0.9164572277,0.9810503129,0.9267011801,1.2548535439,1.1018625405,1.3820413333,1.4186958416,1.3382664757,1.3903868406,1.4000667198
-ercot_1H,1.0291210655,1.0649765384,1.1510446297,1.095463785,1.0976602987,1.137929015,1.2076871071,1.2226080643,1.259822515,1.1754444345,2.6755880612,1.2749705934,1.3214751318,2.6413247553,2.6942610140000003
-ercot_1M,0.7549419552000001,0.8060543809,0.7715311289,1.007020102,0.972573257,0.7729483605,0.9028337317,0.917392765,0.7617082171,0.7879913849,0.7564464177,0.9653005355,0.9042858434,3.4990135369,3.8188806322
-ercot_1W,0.9664287901,0.954652414,0.9324255927,1.0602182535,1.0526167016,0.9613116039,1.2284207021,1.2458511259,2.0949542269,2.089948711,2.0679213662,2.1282361494,2.0789778405,2.0789778405,2.0801083861
-favorita_stores_1D,0.9164120515,0.9681608123,0.9493623591,1.0363827033,0.9798213622,1.0322306624,0.9697812299,1.0613262128,1.1970549978,1.2216416053,1.2378827412,1.2731528133,1.6902367853,2.6356836377,2.6562348012
-favorita_stores_1M,1.7943755173,1.8559124197,1.9983090236,2.0093638587,2.0913151428,2.0865003553,1.9335734731,2.2542926251,1.942621879,2.0381596319,1.9424462204,1.9416452953,2.0967115946,2.0578284127,2.106703069
-favorita_stores_1W,2.0241018122,2.046240588,1.9683866102,2.1277442598,2.1965497094,2.1010925554,2.1226627361,2.3082226469,2.2196008497,2.2969051079,2.3568206927,2.3159830026,2.4938289104,2.4938289104,2.5295781907
-favorita_transactions_1D,0.6846128061,1.0314215727,0.9750458566,0.9750458566,0.9750458566,0.9750458566,1.2251845822,1.1981736735,1.1848435874,1.562168577,1.1813088433,1.2463370552,1.7338216714,2.7303837473,2.7460309898
-favorita_transactions_1M,0.9426020852,1.0893114126,1.1326951058,1.3968989653,1.389630485,1.3584690914,1.2438493019,1.4526874778,1.1521708088,1.2777968435,1.1787496733,1.2743467248,1.3301725476,1.7424291672,1.8848237078
-favorita_transactions_1W,1.2279251366,1.3836394276,1.4283013139,1.5565627963000002,1.4633900473,1.4283013139,1.9116392061,1.8454095028,1.55928265,1.6323137052,1.6473855473,1.7020928499,1.6714139443,1.6714139443,1.7289504361
-fred_md_2025/cee,3.4681775163,3.3490390729,4.4904404116,4.4904404116,4.4904404116,4.4904404116,3.872866885,4.8863751494,3.7449838346,5.8791415649,3.642884743,4.3189751514,9.4371027278,5.9195737804,4.236293641
-fred_md_2025/macro,5.6801711129,5.3069669462,5.8417533276,5.8417533276,5.8417533276,5.8417533276,6.3986770227,6.2236190621,5.7428835095,6.5438633054,5.7936387412,6.0124726904,9.5793076258,6.4047596955,5.9752290488
-fred_qd_2025/cee,2.1919920004,2.0461814654,2.1807036186,1.7726716319,2.2955960423,2.3654952138,2.2915507616,3.6414340592,1.9027740715,1.9543398085,2.1232724277,2.2176909101,4.4448478957,3.675773916,2.1956616384
-fred_qd_2025/macro,3.5367304169,3.5296543064,3.5932767502000003,3.4020827998,3.6155615726,3.6544020032,4.2401411107,4.5427910096,3.6148508861,3.8675028627,3.9044348431,3.8250563211,5.2213314727,4.251884602,3.7928250976
-gvar,0.5781460165,0.576786326,0.5902138174,0.5758663156,0.5933262207000001,0.5962858414000001,0.6741322757,0.7468286472,0.5896099351,0.6171744519,0.5932241947,0.616191821,0.7861696279,0.6701753186,0.6414950038
-hermes,0.6092189047000001,0.6510189412,0.6183988819,0.9852562723,0.7037822323,0.6752116267,0.7049311249,0.8242736568,1.4161705928,1.2129200308,1.6730456492,1.5539343318,2.1461477983,2.1461477983,2.2601295911
-hierarchical_sales_1D,0.5567377299,0.5472671449000001,0.5516009983,0.5468045976,0.550892445,0.550892445,0.5720159668,0.63854811,0.7197387018,0.6445167344,0.7933138017,0.815310861,1.0291569643,1.5929687017,1.6067653205
-hierarchical_sales_1W,0.6161304308000001,0.6208684057,0.6177938232,0.6372998817000001,0.6366963845,0.6366963845,0.6369452495,0.7066222052000001,0.7460571909,0.7472607405,10.476792269,0.7744970082,1.3859393726,1.3859393726,1.416652405
-hospital,0.6860471166000001,0.6884080716000001,0.6797112236,0.7333522854000001,0.6968021756,0.6968021756,0.6961291798,0.8343055996000001,0.6974795864000001,0.7310321414000001,0.7259497104,0.7401804136,0.8075126003,1.0208467827,1.0870231786
-hospital_admissions_1D,0.5544475607,0.5551415729,0.5560606379,0.555491233,0.5555647846,0.5561584192,0.5622795668,0.610265181,0.5569559255000001,0.5556445831,0.5557966863,0.5748023874,0.8571822336,1.3250699981,1.3357575815
-hospital_admissions_1W,0.5763906236,0.5850609146,0.5795336813,0.597604554,0.5862064826,0.5868402215,0.5814141841,0.6410506868,0.5789001687,0.5793359333,0.5783166572,0.5976752248,1.0492127258,1.0492127258,1.0884368132
-jena_weather_10T,0.354328751,0.3893025483,0.3571555639,0.3684159974,0.4178230361,0.4178230361,0.412525166,0.4152438085,0.6729893352,0.6729893352,0.7418250443000001,0.7929160924,0.6729893352,0.7693201112,0.8000027464
-jena_weather_1D,1.1112540336,1.0716473753,1.0903437431,1.1121551269,1.0749683503,1.0749683503,1.1553103934,1.2388075013,1.3391730809,1.3052392106,1.6641987728,1.5312195918,1.7668615982,2.1502001657,2.2588601945
-jena_weather_1H,0.3530167077,0.3556668986,0.3588962212,0.3616362418,0.3668484878,0.3668484878,0.4127616053,0.3803257267,0.4515903467,0.4369650923,0.552924984,0.5933268886,0.6550438031,0.5788947352,0.5844505923000001
-kdd_cup_2022_10T,0.4250791237,0.5333420067,0.5333420067,0.5333420067,0.5333420067,0.5333420067,0.5550654023,0.5333420067,0.7774995242,0.7774995242,0.7469867514,0.7782845321,0.7774995242,0.7544738282,0.8433392069000001
-kdd_cup_2022_1D,0.7035021561,0.6974981294,0.6975119247,0.7036948238,0.7078449047,0.7086991568000001,0.7153283882,0.8018621055,0.7298517236000001,0.720047667,0.7509996402,0.7391822834,0.9005495243,1.1381260567,1.1646512316
-kdd_cup_2022_30T,0.4388772932,0.4319676071,0.505462759,0.4288668087,0.4273561278,0.5612428382,0.5433847052,0.5099402658000001,0.6792689234,0.6406027358,0.7719233326,0.7653501983000001,0.7740434291,0.7638793006,0.7929597862000001
-m5_1D,0.7235519770000001,0.7143626546,0.7292764901000001,0.7292764901000001,0.7292764901000001,0.7292764901000001,1.2544668347,0.8516064277000001,1.2544668347,0.8516619783,0.8527658259,0.8721490279,1.2544668347,1.9603909963,1.9793212176
-m5_1M,0.9769838219,0.9740068956,0.9798127249,1.0439813048,0.9958730245,1.0000578921,1.0016519091,1.0814673882,1.0218515241,1.0455375252,1.1082320899,1.0987392502,1.1398593529,1.1923633059,1.2896192789
-m5_1W,0.9001918640000001,0.9025800625,0.9165191908,0.904968291,0.9069017163,0.9165191908,0.9281611829,0.9747800888,0.9362638046,0.9366737298,0.9530783877,0.9633961601,1.3557578931,1.3557578931,1.3899419915
-proenfo_gfc12,0.6485047642,0.9081089751,0.9171920666,0.9171920666,0.9171920666,0.9171920666,0.8344528413000001,0.9003739311000001,1.3049170998,1.1408344793,2.4308618583,1.414921573,1.1997208201,2.3796009992,2.4805205301
-proenfo_gfc14,0.4301438389,0.7205809869,0.7673985057,0.7673985057,0.7673985057,0.7673985057,0.5148198578000001,0.4208035936,0.9059171447,0.947126445,1.1104064418,1.0554823676,1.0750756438,3.209615645,3.4025952491
-proenfo_gfc17,0.4848892495,0.8894497071,0.9004430830000001,0.9004430830000001,0.9004430830000001,0.9004430830000001,0.6716514716,0.5086387805,1.1416586194,1.1149783841,2.1346457093,1.1469535851,1.3160348949,2.5749628946,2.7046737562
-redset_15T,0.7901011341,0.8326109518,0.7405330074,0.8177766808,1.0409395846,1.2430012316,1.2504740912,1.160404574,1.2313423811,1.2313423811,1.2313423811,10.1219214394,1.2313423811,33.9212721313,35.0901218659
-redset_1H,1.3653252669,1.3365796216,1.366509388,1.3064391449,1.4098475772,2.2790272339,1.3205339155,1.4505298471,1.8587229014,1.8772582072,2.3765166001,4.0716886989,1.9424894097,10.5809680586,10.6448235022
-redset_5T,0.6541973684,0.7872933801,0.7231351044000001,0.7192159039,0.7931599229,1.025913162,0.7111485095000001,0.8873768717,2.6902332367,1.9052087686,1.2243411634,3.3566436475,1.2243411634,12.0595440026,13.0588504389
-restaurant,0.6853328122,0.6816216488,0.6773501614,0.7040430265000001,0.6890372912,0.6890372912,0.6930359174,0.7474752761,0.7085218117000001,0.7558027757,1.0214758859,0.7607880054,0.9837597907,1.6150739536,1.6918666339
-rohlik_orders_1D,0.9592108572,0.9857545101,1.0057460708,1.1350889685,0.9699727195,1.0507521562,1.3410638007,1.1963053977,1.2113521188,1.2661753782,1.4469855127,1.3973344823,1.5544219081,2.9129738594,2.9819524139
-rohlik_orders_1W,1.2996634049,1.3004244287,1.32777854,1.4933704304,1.5315439437,1.4281796145,1.5240499794,1.8923042995,1.3984122308,1.4146777164,1.4189633182,1.3962994461,1.4844422646,1.4844422646,1.4886376798
-rohlik_sales_1D,0.8807894684,1.1480800218,1.0957943217,1.2180556543,1.1696411255,1.1471494654,1.3749816723,1.2026659678,1.2482652223,1.2758180745,1.2661606689,1.2818357414,1.3749816723,1.5460015545,1.5603709005000002
-rohlik_sales_1W,1.274141193,1.4251751964,1.4010447148,1.504562043,1.5157422743,1.5215966312,1.220508006,1.6933358341,1.6455287753,1.8024970943,14.4532204201,1.6547436183,1.9282179039,1.9282179039,1.9690203085
-rossmann_1D,0.2834680567,0.539134486,0.5015787540000001,0.5677454284,0.5274198802,0.5246207313,0.23207865,0.5305513714,0.5781148561,0.5619283369,0.5936917112,0.8315478225,0.9136725214,2.7620723724,2.818179584
-rossmann_1W,0.307693489,0.4815594255,0.4951771693,0.4944119667,0.496872717,0.4871499621,0.2538651882,0.5781252095,0.5013864497,0.5212168044000001,0.5175490828,0.5160293344,0.8987958652,0.8987958652,0.9605854168
-solar_1D,0.5935714299,0.6141032699,0.6181956031,0.6223902809,0.6373379623000001,0.6349957556,0.6146167789,0.6669937254,0.6528759965,0.6575818024,0.6558359500000001,0.6785205054,0.9019637416,1.4067834918,1.4888788408
-solar_1W,0.8952784212,1.1207884648,1.0958866195,1.3917193313,1.6583052048,0.9401913951,0.8695612766,0.9287843032,1.2959542496,1.2798883181,1.2121746876,1.4256110648,1.4228493197,1.4228493197,1.8015134215
-solar_with_weather_15T,0.6769263994,0.8456990474,0.9063360715,0.7839335449,0.8387112868000001,0.8094202444,0.7470578953,0.9625972649,1.1937856846,1.1937856846,2.5289087933000003,3.5850953026,1.1937856846,2.2779930243,2.3830110819
-solar_with_weather_1H,0.7672847522,0.9000403927,0.8153876375,0.876018842,0.9070575976,0.8156760185,0.7005612194,1.1815051767,1.4584163166000002,1.1314165351,2.1818023268,3.3576154004,1.2119606616,2.1807435542,2.1831749916
-uci_air_quality_1D,1.0461166174,1.1279764862,1.205083714,1.2602294231,1.1380484786,1.0919688778,1.1863454763,1.2144755341,1.1225466397,1.2403312496,1.1812884821,1.2334025241,1.4013448948,1.8738938294,1.9477985237
-uci_air_quality_1H,0.7983040593,0.8650050244,0.8769395756,0.8700071749,0.9453838524,0.89902861,0.9312390864,1.0009256435,1.5607085558,1.1923767477,41026.9886208021,1.9559568032,1.3840397362,2.4249545583,2.544297151
-uk_covid_nation_1D/cumulative,7.8258567979,7.6534868379,7.050808391,6.1880495911,6.762809245,8.1573948756,13.0445256974,16.0063232428,7.7115645361,8.6773500345,7.1837995535,18.4454679918,27.0030154455,23.7425594527,17.5575952088
-uk_covid_nation_1D/new,2.0372645115,1.9920440399,2.135267758,2.0391528653,2.1353860487,2.1221392593,2.0764757709,2.2998485754,2.7990662584,3.8389323119,2.7409717093,2.5298732341,2.5728651106,2.6919734344,2.7354032661
-uk_covid_nation_1W/cumulative,2.7834425341,3.1923854702,4.01082831,2.8235902828,3.0143746347,3.4351717559,2.8719957749,5.2476339527,2.2375975817,9.8478063402,2.3989565537,5.097376861,7.7039519752,7.7039519752,4.9437715765
-uk_covid_nation_1W/new,4.9682126678,4.5324987152,3.7830532019,5.0981213862,3.8727833855,4.1483138966,4.1427702993,3.5699175673,5.7411671194,12.6543213595,5.0239734103,5.1818506739,5.1795349212,5.1795349212,5.3904979176
-uk_covid_utla_1D/new,3.7252348002,3.7286792775,3.5117739729,4.0356930401,3.565225656,3.5311824645,3.8009751218,3.6498480124,5.5816729211,7.407131023,5.622786154,5.0552633527,4.5971311302,5.1718851312,5.2745393338
-uk_covid_utla_1W/cumulative,17.4422360952,19.434962205,18.4860520309,16.2859178099,19.3249941782,17.4887255246,16.9124597959,26.4381110934,14.3311431786,13.9770561849,16.3125415616,20.1018108531,24.6697283453,24.6697283453,19.7494072856
-us_consumption_1M,1.4636823053,1.4670456897,1.6053660409,1.5639307325,1.5132208912,1.5162423149,1.5711057336,1.8961848647,1.4864869258,1.598443305,1.4452749249,1.704219721,2.6840778381,1.8701116849,1.6834522226000002
-us_consumption_1Q,1.7235145772,1.8028657315,1.9266298936,1.7074351120000002,1.7957240899,1.764175518,2.6728862979,2.8659918221,1.9078230052,2.0470332513,1.8861010469,2.3452449672,3.3237196122,2.5675143796,2.1897132455
-us_consumption_1Y,3.730472451,3.6344846883,4.007463063,3.8978014747,4.8066289721,4.1076459792,4.1801774688,5.8799625833,3.7862940987,3.6882837957,4.0805825682,5.0841302662,6.5846226659,6.5846226659,4.9461376338
-walmart,0.6478016418,0.7074857338,0.6794198494,0.9071550498,0.8447389691,0.7740159174,0.6618592710000001,0.8422492473000001,1.2166093946,1.0205417736,21704350706303.93,1.4674936934,2.0341238516,2.0341238516,2.2528910444
-world_co2_emissions,2.6702032785,2.6434689128,2.8764182979,2.7160328031,2.8754184288,2.7544343248,2.7200799174,3.6825226706,2.6879348969,2.8223304034,7.7240531151,2.8254620295,3.1550152472,3.1550152472,2.755800442
-world_life_expectancy,1.1865747514,1.1086152308,1.2103954021,1.6394210102,1.7852750057,1.3452468722,1.1492708484,1.6015213365,1.3050094069,1.2829496791,1.3015724131,1.4512016278,1.8297624985,1.8297624985,1.5824689261
-world_tourism,3.0519540955,3.0521218421,3.5616996609,3.2075602319,3.2640026065,3.1644023349,2.7953196676000003,4.3603502979,2.552402438,2.6184635789,2.8819679863000003,2.5742928529,3.2013302671,3.2013302671,2.3982302133

tables/pivot_SQL_baseline_imputed.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TiRex,TimesFM-2.5,Toto-1.0,Moirai-2.0,Chronos-Bolt,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
-ETT_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-ETT_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ETT_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ETT_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1H,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-LOOP_SEATTLE_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-SZ_TAXI_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-SZ_TAXI_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1209,False,False,False,False,False,False,False,False,False,False,True,False,False,False,False
-boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
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-boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
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-boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
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-boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_10T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-jena_weather_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_10T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1D,False,False,False,False,False,False,True,False,True,False,False,False,False,False,False
-m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc12,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc14,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc17,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_15T,False,False,False,False,False,False,False,False,True,True,True,False,False,False,False
-redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_5T,False,False,False,False,False,False,False,False,False,False,True,False,False,False,False
-restaurant,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1D,False,False,False,False,False,False,True,False,False,False,False,False,False,False,False
-rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_SQL_leakage_imputed.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TiRex,TimesFM-2.5,Toto-1.0,Moirai-2.0,Chronos-Bolt,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
-ETT_15T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-ETT_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-ETT_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-ETT_1W,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_5T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-SZ_TAXI_15T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-SZ_TAXI_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_5T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1209,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1282,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1631,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1676,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_619,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1D,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1W,False,False,True,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/cee,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/macro,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1W,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-hospital,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_10T,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-jena_weather_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-jena_weather_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_10T,False,True,True,True,True,False,False,True,False,False,False,False,False,False,False
-kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1D,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1W,False,False,True,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc12,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc14,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc17,False,False,True,True,True,False,False,False,False,False,False,False,False,False,False
-redset_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-restaurant,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_WAPE.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TimesFM-2.5,TiRex,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Naive,Seasonal Naive,Drift
-ETT_15T,0.1128598548,0.1167248718,0.1158377729,0.121559811,0.122192445,0.1146202493,0.1146202493,0.1157490913,0.1473227948,0.1473227948,0.2089927338,0.1327521257,0.202845639,0.1473227948,0.209491349
-ETT_1D,0.3210531384,0.332150948,0.3280948972,0.3407840222,0.3959747152,0.3275848009,0.3275848009,0.3646975075,0.3275626761,0.3554786793,0.3392487691,0.3405146034,0.336139462,0.3566862594,0.3456173895
-ETT_1H,0.2467289709,0.245201607,0.2477475438,0.2432126313,0.2553246886,0.2464663107,0.2464663107,0.2547681354,0.2621275507,0.2729976766,0.3240313299,0.2708169587,0.3389070071,0.286422462,0.3517438181
-ETT_1W,0.5228870779,0.4956519336,0.4771292359,0.4972649187,0.5109347999,0.4599850744,0.4599850744,0.5208597481,0.4587044328,0.5033163548,0.455156821,0.4891462862,0.3963210046,0.3963210046,0.4451834112
-LOOP_SEATTLE_1D,0.0363369947,0.0359623253,0.0367103025,0.0387991458,0.0365180448,0.0373221384,0.0373221384,0.0372882528,0.0378798334,0.0375884525,0.0379914595,0.0389688019,0.0786971625,0.0445690587,0.0891125299
-LOOP_SEATTLE_1H,0.0681990329,0.0643115029,0.0704373386,0.0749491051,0.0731078316,0.0797934189,0.0797934189,0.077676107,0.1172784954,0.1172784954,0.1460618883,0.1024438366,0.1526884101,0.1172784954,0.1758716479
-LOOP_SEATTLE_5T,0.0736883409,0.0792618439,0.0750445005,0.075644061,0.0858088784,0.0886344958,0.0886344958,0.0829992138,0.1069220781,0.1080897018,0.1085262727,0.1108838413,0.1121638626,0.0883255631,0.130171527
-M_DENSE_1D,0.0833181143,0.0935403809,0.0993285432,0.1092759416,0.0926615007,0.0985291574,0.0985291574,0.0968305223,0.1192791395,0.1260855269,0.1300925907,0.1322432969,0.196977134,0.1419847511,0.2006916501
-M_DENSE_1H,0.1462709986,0.13799605,0.1467316881,0.1535322428,0.1593384311,0.1466876373,0.1466876373,0.1584326059,0.2484246612,0.2454276025,0.3140071735,0.2921647772,0.6536297143,0.2611668751,0.707449168
-SZ_TAXI_15T,0.2391328573,0.2409728497,0.2404616728,0.2440994486,0.2500552535,0.2437216282,0.2437216282,0.245247978,0.3343059897,0.3343059897,0.3189420968,0.2728340209,0.3568555236,0.3343059897,0.3669907093
-SZ_TAXI_1H,0.1627460048,0.1730356291,0.1678203642,0.1653927863,0.1798963994,0.1713460237,0.1713460237,0.2091502994,0.1850999221,0.2079726383,0.6624499261,0.2595272139,0.246570535,0.2122471854,0.290869683
-australian_tourism,0.090568915,0.0948839262,0.1068701223,0.1337763593,0.0927634016,0.1308895275,0.1434193179,0.0969211906,0.0957876109,0.1101633608,0.0970393047,0.1052965894,0.1803925335,0.1130525246,0.199330695
-bizitobs_l2c_1H,0.3308651592,0.3901138266,0.4282031118,0.5299379429000001,0.360369738,0.4081446098,0.4081446098,0.4650631395,1.3223615305,1.7735276449,1.2847489447,1.3980487641,1.3024706409,2.2812914532,1.3151569486
-bizitobs_l2c_5T,0.4957357183,0.9057201819,1.4135781384,1.2221172807,0.8823374127,1.3254390534,1.3254390534,0.6893105085,1.3437154961,1.5979523685,1.3366881806,1.6561117344,1.2818957102,2.2837712012,1.8269103833
-boomlet_1062,0.5680818457,0.5789273618,0.5749673398,0.5677611841,0.5832466359,0.5945871048,0.6012691446,0.6355941124000001,0.7826401234,0.7457671713,0.8238335352,0.8386591385000001,1.0029399408,0.8355847068000001,1.0990104974
-boomlet_1209,0.3048901003,0.3045249204,0.3234299188,0.2916179846,0.3865331056,0.3292905567,0.3271624692,0.3340434651,0.4339859435,0.4278108916,0.4784097772,0.456201244,0.4388518186,0.4784097772,0.5110153038
-boomlet_1225,0.1220172789,0.1242312214,0.1230602603,0.1206233559,0.1326060004,0.1278310667,0.1331294786,0.1333109416,0.153843496,0.1446868773,0.1598025897,0.1596047961,0.195260353,0.195260353,0.1988768318
-boomlet_1230,0.3054680914,0.2999179457,0.3117769108,0.2909953139,0.3497826156,0.3094188715,0.3241073897,0.3178766378,0.3894998971,0.3998710697,0.4274646386,0.4040077124,0.3912469672,0.4654206108,0.4504819196
-boomlet_1282,0.3551724824,0.3418466096,0.3447100926,0.3443926057,0.3517501049,0.3639267584,0.3918061855,0.360391448,0.4329732117,0.4049750815,0.4705070178,0.471762625,0.5673586508,0.5673586508,0.5815167152
-boomlet_1487,0.2009541858,0.1978987699,0.2042655856,0.1894979433,0.2418147345,0.2079859222,0.2074861592,0.2194652483,0.2612758556,0.259043758,0.2614417874,0.270651306,0.2881606094,0.2823812783,0.3361342464
-boomlet_1631,0.3118222298,0.3155328254,0.3227136546,0.3156198487,0.3554736745,0.322950287,0.3246407648,0.3276903199,0.4303123065,0.4303123065,0.3481223856,0.3499573969,0.3828907337,0.4303123065,0.3956788492
-boomlet_1676,0.323739112,0.3195228016,0.3219743376,0.3173684891,0.3439016851,0.3274155865,0.3288848916,0.3249830057,0.4490416802,0.4490416802,0.3539671682,0.3605949771,0.3738144227,0.4490416802,0.3871649546
-boomlet_1855,0.1587321404,0.1647552303,0.157446321,0.1576757028,0.1950387305,0.1619848815,0.1634840172,0.1777051742,0.2153578225,0.2241676514,0.2190390133,0.2310231853,0.2028796298,0.255325092,0.2044042459
-boomlet_1975,0.0905538368,0.1160976808,0.1343890613,0.0860611707,0.1347071896,0.1478435607,0.1244086675,0.1286565167,0.3327476291,0.3661623288,0.4015633572,0.3240569453,0.4014330303,0.5204730918,0.4021568225
-boomlet_2187,0.2656522722,0.2903383515,0.2640573038,0.2836815666,0.3217076673,0.2926881402,0.2890647493,0.3149817281,0.4073318877,0.4429809169,0.4339327821,0.441407778,0.403332821,0.465180005,0.4079644265
-boomlet_285,0.163745336,0.1907992813,0.1807812872,0.1596092123,0.1742876602,0.2203877039,0.2555644857,0.2649652282,0.3599062982,0.34433789,0.3622854223,0.3717445356,0.6725665377000001,0.6725665377000001,0.6908623011
-boomlet_619,0.2998264338,0.3117435689,0.317407313,0.2789135486,0.3036044294,0.3062168618,0.4683247317,0.3109702097,0.8714422486000001,0.5880218188,0.9528967892,0.8820872245,0.9458725577,0.9458725577,0.9676378967
-boomlet_772,0.1571995007,0.160275506,0.16101018,0.1544767227,0.1705900793,0.1698505863,0.1845645847,0.1812428949,0.2635364999,0.2378165958,0.3077275646,0.2834336899,0.2919496069,0.2919496069,0.2993941878
-boomlet_963,0.381703259,0.397875566,0.3901485342,0.3792164679,0.415064609,0.4169379192,0.4316260388,0.4042673384,0.5013279652,0.5164873201,0.5458354699,0.5049570994,0.5169750445,0.5169750445,0.5254156838
-ecdc_ili,0.3759767383,0.4172024563,0.4242536306,0.4693168223,0.3879963517,0.4756085008,0.4956143111,0.5432749093,0.5675356537,0.5880377501,0.6168868512,0.5694230199,0.5534411848,0.5534411848,0.5846513778
-entsoe_15T,0.0421964779,0.0416073938,0.0420496514,0.0522911714,0.0426369185,0.0423905659,0.0428303072,0.0564987165,0.067677392,0.067677392,0.4080646552,0.0539835229,0.1434365042,0.067677392,0.1567171741
-entsoe_1H,0.0329469321,0.0358633269,0.0363473815,0.0365800265,0.0333449995,0.0395727229,0.0341011005,0.0554685768,0.0833389089,0.0854086541,0.1569610283,0.0836396273,0.1571170386,0.0792450108,0.1671798959
-entsoe_30T,0.0367302982,0.0473410125,0.0399606742,0.0378812171,0.0390591249,0.038267585,0.0378308199,0.0519962882,0.0767360015,0.090934383,0.241174338,0.0721444593,0.1462993909,0.0870952165,0.1450240765
-epf_be,0.1155560069,0.11255784,0.1230011668,0.1323491182,0.1179565661,0.1227849199,0.1357350046,0.1303241445,0.1754423061,0.1986222906,0.2487520538,0.1881915066,0.237576263,0.1840044525,0.2400494121
-epf_de,0.291016902,0.5914115101,0.5786662247000001,0.6772215117,0.3069136301,0.5364827175,0.5606896855,0.5721812874000001,0.6023222137,0.6818832021,0.6107009687,0.6459928796000001,0.6107063688000001,0.7489839181,0.6189188711
-epf_fr,0.0680296246,0.0771284029,0.0797966965,0.0836794117,0.0618426707,0.079128382,0.0876754681,0.0812654203,0.1133474851,0.1532071637,0.1492719604,0.1293735383,0.1702834178,0.1298259975,0.1727491379
-epf_np,0.0381853646,0.0639044802,0.0554376821,0.0616097255,0.0386706958,0.0542902095,0.0566988389,0.0496031295,0.0682523511,0.078622775,0.1040808285,0.0688152587,0.1036567884,0.0796461385,0.1036498375
-epf_pjm,0.0821098216,0.0941735437,0.08936894,0.1030592872,0.092521411,0.0975614782,0.0937614741,0.0934227692,0.0933501931,0.098476214,0.1716884077,0.1198625589,0.1717319913,0.1016348289,0.1742728941
-ercot_1D,0.0765202647,0.0756690312,0.075154368,0.0810110342,0.0898239311,0.0819215234,0.0793944309,0.0776411118,0.1129253348,0.1103588376,0.117865942,0.1169790924,0.1179466095,0.1245740173,0.1188580243
-ercot_1H,0.0691227347,0.0764717873,0.0705278089,0.0721937651,0.0795933543,0.0716643825,0.0730120171,0.074077414,0.0829790251,0.0749658542,0.1808560818,0.081462577,0.1561551619,0.0818516593,0.156225403
-ercot_1M,0.0474946227,0.0484172593,0.0524891056,0.0669821394,0.05342761,0.0656316509,0.0511789478,0.051032866,0.0481692294,0.0493777886,0.0470737303,0.0593784221,0.266211768,0.0529377862,0.2937269539
-ercot_1W,0.0589590447,0.059191324,0.0586846132,0.0654530676,0.0684465686,0.0638612125,0.0599824842,0.0685063219,0.1453860532,0.1429765524,0.1469057526,0.1458645716,0.1442763271,0.1442763271,0.1446279654
-favorita_stores_1D,0.1382725291,0.1452467814,0.1518204361,0.1783829153,0.1485749632,0.1568098612,0.1742722556,0.1561883375,0.1788716882,0.1934442326,0.1871851772,0.1834860831,0.3056042403,0.2519854262,0.3071700007
-favorita_stores_1M,0.1303168498,0.2102531455,0.1893665791,0.1978285983,0.1158652566,0.2355426997,0.263652347,0.2560960427,0.1466952749,0.1385348588,0.1568281725,0.1535032243,0.1232989542,0.1900056936,0.1430777609
-favorita_stores_1W,0.128928905,0.1308373965,0.1345634833,0.1511486098,0.1259902738,0.1555078961,0.1579625078,0.1497412801,0.1436039165,0.1500959687,0.148442591,0.1443628684,0.1528069191,0.1528069191,0.1540484183
-favorita_transactions_1D,0.0611646157,0.0873474585,0.0820303043,0.0873474585,0.0773345503,0.0873474585,0.0873474585,0.0798803577,0.1005134217,0.1204928338,0.1010010868,0.0961165557,0.1561206006,0.1460640723,0.1564211233
-favorita_transactions_1M,0.0719262225,0.0741879094,0.0772057417,0.0891149079,0.0822258855,0.0863027538,0.0940793492,0.0944761617,0.0746712369,0.0833974167,0.075680572,0.0943706955,0.0830064558,0.0907197162,0.0979339545
-favorita_transactions_1W,0.0562127216,0.0628811317,0.0590786255,0.0686957029,0.070972002,0.0671727598,0.0628811317,0.0715127803,0.0683781244,0.0730921635,0.0708877588,0.0703343528,0.0709917221,0.0709917221,0.0733929446
-fred_md_2025/cee,0.0808948614,0.0893563021,0.0771422869,0.0893563021,0.0853241075,0.0893563021,0.0893563021,0.1094554538,0.0880950077,0.101570566,0.0874256578,0.0903405098,0.0912865269,0.1825383855,0.0899785994
-fred_md_2025/macro,0.0797789793,0.0832975223,0.0786031675,0.0832975223,0.0845430854,0.0832975223,0.0832975223,0.0866654116,0.0850931924,0.0913136788,0.0855845023,0.086868012,0.0864161201,0.1316369599,0.0862326509
-fred_qd_2025/cee,0.165729059,0.1558658059,0.146726247,0.1320567852,0.1495637866,0.1528913536,0.1553945123,0.2050228651,0.1535186178,0.1628828034,0.163525425,0.1604369975,0.1646972333,0.2083851339,0.1613227853
-fred_qd_2025/macro,0.1070199623,0.1036789802,0.1035667702,0.1019809656,0.1155847,0.1058608346,0.1074347071,0.1156803335,0.1102426328,0.1150225402,0.1130458034,0.1135594273,0.112650905,0.1422945023,0.1116643421
-gvar,0.0184834709,0.0187466032,0.0185361033,0.0183949311,0.0235774602,0.0185879215,0.0189676567,0.0219536834,0.018235926,0.0184923184,0.0183902988,0.0183729022,0.0185624258,0.0239340235,0.017942446
-hermes,0.0028845991,0.0029142732,0.0030851755,0.0043706582,0.003404404,0.0033616596,0.003208007,0.0036552635,0.0078905979,0.0063903024,0.0088396827,0.0081041083,0.0087068714,0.0087068714,0.0088381628
-hierarchical_sales_1D,0.7004948258,0.6943961620000001,0.6893573284000001,0.6904558182,0.7258024514,0.6949587405000001,0.6949587405000001,0.7362602293,0.8618716657000001,0.80726372,0.9335796714,0.9374910116,1.0676994622,1.0205006063,1.0792345285
-hierarchical_sales_1W,0.4327760696,0.4307300597,0.4324219674,0.4364300489,0.4384448647,0.4394375533,0.4394375533,0.4750745565,0.5484556556,0.5603942513,0.6528081954,0.5616531372,0.7169096172,0.7169096172,0.73438465
-hospital,0.0955333542,0.0891746506,0.0908275228,0.0951568279,0.0931978375,0.0947973169,0.0947973169,0.1026285514,0.0920567866,0.1056054849,0.0927931517,0.0992142502,0.117495615,0.0954293236,0.1240455657
-hospital_admissions_1D,0.5318945218,0.533603767,0.532626473,0.5320996389,0.5375386306000001,0.5332016895,0.5337376775,0.5358891719000001,0.5351516929,0.5348244904,0.5349606606,0.5510431179,0.7243229402,0.7622172844,0.7295191623
-hospital_admissions_1W,0.2115394623,0.212422525,0.2142321087,0.2188375845,0.2122697207,0.2152673571,0.214606831,0.2139370313,0.2126255133,0.212284724,0.2122781935,0.2190619932,0.2933695345,0.2933695345,0.30397943
-jena_weather_10T,0.2751553417,0.257587004,0.2777656166,0.2483462442,0.2988471952,0.3021136379,0.3021136379,0.3908659499,0.7879570897,0.7879570897,0.3862366249,0.3075604132,0.340848519,0.7879570897,0.5317361675
-jena_weather_1D,0.2763651371,0.2713017484,0.2731164291,0.2763100538,0.2843381079,0.2784405042,0.2784405042,0.306889223,0.3410053076,0.3394734252,0.3844678975,0.3648304911,0.3843897356,0.4190868366,0.4042778883
-jena_weather_1H,0.2537129368,0.2603537007,0.2506694068,0.2467585944,0.2825195241,0.2519024358,0.2519024358,0.435202554,0.3278975237,0.9374862075,0.3521687292,0.2954772817,0.328573375,0.7711870382,0.3343243943
-kdd_cup_2022_10T,1.3604882419,1.5299117744,1.5299117744,1.5299117744,2.9485943079,1.5299117744,1.5299117744,1.5299117744,3.3046866596,3.3046866596,2.0494638503,2.1184663236,2.0376436353,3.3046866596,2.1939117908
-kdd_cup_2022_1D,0.7152167618,0.7140720218000001,0.722069484,0.709543103,0.729861784,0.7286467254,0.7228399217,0.7455375254000001,0.7985714674000001,0.7844091415000001,0.8128879607,0.8038047969000001,0.8071428537,0.9081870615,0.8271189928
-kdd_cup_2022_30T,1.5203950584,1.9992301464,1.5899849832,1.6237983048,2.7372743011000003,1.3630170584,2.6893893838,1.8708190978,2.1525859416,2.579102248,2.3230794907,2.3053818822,2.2917796373,3.122066617,2.3595300138
-m5_1D,0.7033757567000001,0.7110159993,0.7024515271,0.7110159993,0.9158941507,0.7110159993,0.7110159993,0.735691011,0.9158941507,0.7716780305000001,0.7714367509000001,0.7779017687,1.0240883827,0.9158941507,1.0368541479
-m5_1M,0.4288782179,0.4248805046,0.4305847585,0.459651649,0.4364556372,0.4363167882,0.4455042779,0.4508921802,0.4417713284,0.4600670636,0.4605289102,0.491009295,0.4776364863,0.5107533932,0.5485711098
-m5_1W,0.4283166826,0.4334180355,0.4280506968,0.4278151095,0.4357982576,0.4286454916,0.4334180355,0.4258472919,0.4386759996,0.4387526214,0.4468327463,0.4475453794,0.5041812062000001,0.5041812062000001,0.5192105174
-proenfo_gfc12,0.0695905495,0.09096132,0.0953878213,0.09096132,0.0878337689,0.09096132,0.09096132,0.0814341474,0.140745189,0.1193260785,0.2331060618,0.1359507203,0.2325648576,0.1285183787,0.2455491483
-proenfo_gfc14,0.0263798785,0.0454557415,0.0447395099,0.0454557415,0.0314071138,0.0454557415,0.0454557415,0.022570387,0.0540108977,0.0571345754,0.0646636074,0.0579787859,0.1831386365,0.0586917939,0.1959836841
-proenfo_gfc17,0.0395739379,0.0744532838,0.0764463827,0.0744532838,0.0562544549,0.0744532838,0.0744532838,0.0343229823,0.0960090132,0.0943354612,0.1663142737,0.0931363594,0.1911468029,0.1077746021,0.2018542446
-redset_15T,0.2581138387,0.3049023062,0.2957181528,0.3032550395,0.2824990153,0.3051173747,0.3266223073,0.3414731026,0.3900802583,0.3900802583,0.3900802583,0.4040436536,0.5721419543,0.3900802583,0.5907099247
-redset_1H,0.2086091325,0.2327233687,0.2140746087,0.2279421866,0.2258856788,0.2229073554,0.2425440878,0.263667053,0.302437371,0.3273749292,2.3069740623,0.371760726,0.4976614714,0.3300818771,0.5004897475
-redset_5T,0.3293815106,0.3443454772,0.3807104856,0.343882215,0.3484145552,0.3707561105,0.4300963342,0.3967622221,0.6020359665,0.6915958703,0.4487639129,0.6252421260000001,0.6784450889,0.4487639129,0.7538361549
-restaurant,0.358380679,0.3569885604,0.3583980985,0.3731744513,0.3642114922,0.3649469204,0.3649469204,0.3637035079,0.3710609339,0.3996725455,0.4009865783,0.3923323192,0.612521477,0.483283326,0.6468708888
-rohlik_orders_1D,0.0560522966,0.0590882786,0.0571725398,0.0646083735,0.0655876726,0.0552303962,0.0613666862,0.0667062812,0.0628061324,0.0740682058,0.0657169782,0.0664489299,0.1082820654,0.0827216394,0.1096319526
-rohlik_orders_1W,0.0503461543,0.0541592546,0.0520526886,0.0591418616,0.0652046457,0.0612039004,0.0569869973,0.0675320886,0.0573217526,0.0584325351,0.0581583686,0.0573223703,0.058363314,0.058363314,0.059100195
-rohlik_sales_1D,0.2762769461,0.3586171567,0.3783946335,0.3974789381,0.4345725775,0.3820689023,0.3783308864,0.3645052612,0.4056582451,0.4094604254,0.4184724987,0.4130674899,0.4717368782,0.4345725775,0.4772062898
-rohlik_sales_1W,0.2291195095,0.2667129934,0.2736291289,0.2812094092,0.2151686251,0.2871065438,0.2846268117,0.292871207,0.2905908525,0.3070147634,0.3002878428,0.294593662,0.311866194,0.311866194,0.3224963248
-rossmann_1D,0.1197794937,0.2085952148,0.2251284003,0.2320613816,0.0979018956,0.2214855403,0.2176495418,0.2092144758,0.2277583107,0.2223706722,0.2360617861,0.2557922274,0.5480500698,0.2692451894,0.5516847163
-rossmann_1W,0.0964419879,0.1802897658,0.1696977001,0.1733398307,0.0791731263,0.1759210136,0.1760291811,0.1854728833,0.1783457696,0.1835633125,0.1836160459,0.1812564228,0.2192445789,0.2192445789,0.2424932998
-solar_1D,0.2379390113,0.2471472979,0.2452487931,0.2500299245,0.2466564849,0.2569396086,0.2532424137,0.2471724987,0.2494917773,0.2527682468,0.2511778109,0.2619624853,0.3013429433,0.3093768284,0.3458185181
-solar_1W,0.2001198977,0.246641323,0.2531014979,0.3023860455,0.1849420816,0.3419925272,0.2132549584,0.1854887605,0.3008096814,0.2914025486,0.2775774002,0.3495942652,0.326290369,0.326290369,0.4388745129
-solar_with_weather_15T,1.1523838531,1.4700251039,1.4235610306,1.1480220269,1.1661535196,1.3761328127,1.2911253296,1.5058858633,1.311486863,1.311486863,1.4215377569,1.5023058228,1.0179969072,1.311486863,1.2097884297
-solar_with_weather_1H,1.2584227458,1.2011471927,1.5260583486,1.4068397004,0.9096449066,1.4828350302,1.3260020718,1.4480908126,1.2681774691,1.2902403221,1.1464893281,1.5474270582,1.1464484423,1.2338736704,1.1473927319
-uci_air_quality_1D,0.2703591761,0.3069281131,0.2942162712,0.3233137009,0.3105711883,0.2915518175,0.2842696743,0.2892992957,0.2860204957,0.3229408102,0.2790713324,0.2964460064,0.4314649024,0.3624341339,0.456071114
-uci_air_quality_1H,0.3364187218,0.371518901,0.3697669104,0.3658213586,0.3857780203,0.3903881013,0.3758005321,0.400704702,0.4116057828,0.4518040374,4.0641790986,0.4167998955,0.5250781342,0.4562693149,0.5444705039000001
-uk_covid_nation_1D/cumulative,0.0188431359,0.0172808705,0.0159650212,0.0158421656,0.0419449611,0.0173290129,0.0245629952,0.0455631685,0.0214682838,0.0188173242,0.0182502112,0.047922572,0.0579707756,0.0695790831,0.0458026743
-uk_covid_nation_1D/new,0.4026392743,0.3887864605,0.3817302555,0.3457811862,0.4174416304,0.3626234081,0.4166112363,0.4168794766,0.4527084805,0.4697780043,0.4591069408,0.4808448859,0.453881491,0.4987584807,0.4637164548
-uk_covid_nation_1W/cumulative,0.027964653,0.0420784587,0.0336664002,0.0338043142,0.0386651377,0.0277593429,0.0386007642,0.0525751226,0.0229528998,0.0670678648,0.0234671999,0.0519616026,0.0820604023,0.0820604023,0.0507871248
-uk_covid_nation_1W/new,0.7566535249,0.4691022187,0.6047020033,0.7679864317,0.626670666,0.5119306892000001,0.533506915,0.4364076927,0.4829665609,0.7762918919,0.4576220214,0.4545178637,0.4544002153,0.4544002153,0.4722695798
-uk_covid_utla_1D/new,0.4765254885,0.4222944811,0.4611622334,0.4977396607,0.4566554457,0.4184653059,0.4370788723,0.4043421954,0.5653157011000001,0.6831767932,0.5764029175000001,0.5472069114,0.560081014,0.5015514418,0.5699051917
-uk_covid_utla_1W/cumulative,0.1985057145,0.2217276141,0.2228221811,0.1818175469,0.1957054086,0.2224105477,0.1935101479,0.2817648932,0.1562370956,0.1529182278,0.1736827634,0.2025926664,0.2501822181,0.2501822181,0.198870188
-us_consumption_1M,0.0221474885,0.0262938315,0.0229767126,0.0234064611,0.0282917617,0.0248397756,0.025038723,0.0353930782,0.0240289312,0.0266722746,0.0232927346,0.0304996581,0.0367178954,0.0583924714,0.0302986122
-us_consumption_1Q,0.0334895655,0.0319971864,0.0341675981,0.0322923365,0.0508262731,0.0323032217,0.0322679118,0.0549728539,0.0338116094,0.0334090083,0.0345600061,0.0455343083,0.0568597171,0.0723187622,0.0433424644
-us_consumption_1Y,0.0613212422,0.0577691134,0.0570256105,0.0712195709,0.0673417844,0.1083728828,0.0879293952,0.1102996588,0.0638795553,0.0521022394,0.0765756212,0.1074392479,0.1702565216,0.1702565216,0.1056817938
-walmart,0.0933870822,0.1015809998,0.1053567082,0.1384944916,0.0943299681,0.1315481663,0.1172761619,0.1211136207,0.1773376018,0.1595972031,0.2950883508,0.1852148771,0.1967352033,0.1967352033,0.2281249762
-world_co2_emissions,0.089445687,0.0879381941,0.0871799563,0.0830718834,0.0868360951,0.093244922,0.0897831822,0.1067606712,0.081172869,0.0819489691,0.0843222712,0.0831107938,0.0984961833,0.0984961833,0.0830490989
-world_life_expectancy,0.0116658187,0.0112725816,0.0107911893,0.0150144563,0.0108183488,0.0176806053,0.0130603154,0.0145865259,0.0126706732,0.0130623952,0.0126002091,0.0137058759,0.0171300128,0.0171300128,0.0147433012
-world_tourism,0.1209655702,0.1209704466,0.1137896851,0.1073285565,0.0676485132,0.114122387,0.1171618253,0.1553527489,0.0681912079,0.073036734,0.0937603042,0.0556104314,0.1106580384,0.1106580384,0.0549742542

tables/pivot_WAPE_baseline_imputed.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TimesFM-2.5,TiRex,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Naive,Seasonal Naive,Drift
-ETT_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-ETT_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ETT_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ETT_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1H,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-LOOP_SEATTLE_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-SZ_TAXI_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-SZ_TAXI_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1209,False,False,False,False,False,False,False,False,False,False,True,False,False,False,False
-boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1282,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1631,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-boomlet_1676,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_619,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_10T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-jena_weather_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_10T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1D,False,False,False,False,True,False,False,False,True,False,False,False,False,False,False
-m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc12,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc14,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc17,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_15T,False,False,False,False,False,False,False,False,True,True,True,False,False,False,False
-redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_5T,False,False,False,False,False,False,False,False,False,False,True,False,False,False,False
-restaurant,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_WAPE_leakage_imputed.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TimesFM-2.5,TiRex,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Naive,Seasonal Naive,Drift
-ETT_15T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-ETT_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-ETT_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-ETT_1W,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_5T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-M_DENSE_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-M_DENSE_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-SZ_TAXI_15T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-SZ_TAXI_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_5T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1209,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1282,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1631,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1676,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_619,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1D,False,True,False,True,False,True,False,False,False,False,False,False,False,False,False
-favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1W,False,True,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/cee,False,True,False,True,False,True,False,False,False,False,False,False,False,False,False
-fred_md_2025/macro,False,True,False,True,False,True,False,False,False,False,False,False,False,False,False
-fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1W,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-hospital,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_10T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-jena_weather_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-jena_weather_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_10T,False,True,True,True,False,True,False,True,False,False,False,False,False,False,False
-kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1D,False,True,False,True,False,True,False,False,False,False,False,False,False,False,False
-m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1W,False,True,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc12,False,True,False,True,False,True,False,False,False,False,False,False,False,False,False
-proenfo_gfc14,False,True,False,True,False,True,False,False,False,False,False,False,False,False,False
-proenfo_gfc17,False,True,False,True,False,True,False,False,False,False,False,False,False,False,False
-redset_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-restaurant,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_WQL.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TiRex,TimesFM-2.5,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
-ETT_15T,0.0881409166,0.0914609728,0.0925313491,0.0951691601,0.0966499793,0.0933383153,0.0933383153,0.0969683943,0.1224205307,0.1224205307,0.1889663006,0.170235374,0.1224205307,0.2051287363,0.2108337564
-ETT_1D,0.2599714564,0.2612366718,0.267888696,0.2741924035,0.3179643477,0.2669855109,0.2669855109,0.3129149392,0.3227852613,0.3320927513,0.3649904644,0.360480833,0.3642035745,0.3538102937,0.3621092269
-ETT_1H,0.1929432658,0.1923900276,0.1916135933,0.189981304,0.2034494101,0.2059476241,0.2059476241,0.2146782131,0.2620350459,0.2275419988,0.3554423083,0.387905148,0.2597090183,0.4903424451,0.5136644933
-ETT_1W,0.4180629801,0.3834384084,0.3998766685,0.3901701003,0.4148319078,0.3737931634,0.3737931634,0.4643316617,0.4495446321,0.4761461158,0.4577441806,0.4986518854,0.4614224698,0.4614224698,0.4961494694
-LOOP_SEATTLE_1D,0.0294338102,0.029926259,0.0292182088,0.0313094796,0.0295572487,0.0304002738,0.0304002738,0.0326217509,0.0310410872,0.0306907047,0.0311914855,0.0325479384,0.0397058571,0.0919262659,0.0978699313
-LOOP_SEATTLE_1H,0.0547949415,0.0562291392,0.0529133781,0.0599067065,0.0580208445,0.0662523771,0.0662523771,0.0664487294,0.1307515175,0.1307515175,0.2388070951,0.2528752457,0.1307515175,0.3038598558,0.3206568541
-LOOP_SEATTLE_5T,0.0574848141,0.0594513843,0.0643709018,0.0606719844,0.0689450513,0.0781392419,0.0781392419,0.0714930823,0.1151931596,0.1111745205,0.1270606325,0.121969972,0.081199984,0.1936404655,0.210202567
-M_DENSE_1D,0.0712116921,0.0821914373,0.0773717212,0.0906527349,0.0776412798,0.0817335467,0.0817335467,0.0844361527,0.1131422312,0.1051447846,0.1323504645,0.1459052349,0.1367572058,0.2323397334,0.2363624963
-M_DENSE_1H,0.119830846,0.1208851977,0.1143436056,0.1267713853,0.1296769427,0.1209575592,0.1209575592,0.1339606641,0.2346087824,0.2211301613,12.8518608164,0.7918015547,0.2731296131,0.84829413,0.8946267050000001
-SZ_TAXI_15T,0.1878149412,0.1889248146,0.1894354422,0.1918294193,0.2040341404,0.197437778,0.197437778,0.2118998785,0.2672484294,0.2672484294,0.9991056751,0.2434722417,0.2672484294,0.6619538384,0.6804962505000001
-SZ_TAXI_1H,0.1329118925,0.1368741884,0.1394973218,0.1399772683,0.1666049443,0.1447212932,0.1447212932,0.1764986928,0.2449234495,0.2187159408,4510502977.541462,0.3559208311,0.2910650923,0.9157567452,1.0097515601
-australian_tourism,0.0714093583,0.0861105624,0.0761527549,0.1044141415,0.07310177,0.1053900171,0.1140447009,0.0836038879,0.0747720206,0.0866216851,0.0760110551,0.0823354804,0.0905870679,0.1922018184,0.2141353268
-bizitobs_l2c_1H,0.2574969401,0.3395892762,0.3062390652,0.4180692521,0.3020415377,0.3259227696,0.3259227696,0.4028991374,1.2739406784,1.4044947577,1.4288888475,1.3542131559,2.0969368333,1.3379066541,1.3508241876
-bizitobs_l2c_5T,0.3972981771,1.2724528657,0.7904916189000001,1.0846777557,0.6797159368,1.2750077013,1.2750077013,0.5857720953000001,1.5128526941,1.8771775481,1.6113720868,1.6069322946,2.2877704895,1.4507804453,1.731649134
-boomlet_1062,0.4575715743,0.462540047,0.4710064002,0.4560339911,0.4854876351,0.4872201343,0.5409239931000001,0.5396810790000001,0.7822110511,0.5907259586,1.0658179181,1.085539266,0.7418110944,3.1996986673,3.4646472151
-boomlet_1209,0.2624618712,0.2759885511,0.2609844697,0.2511164388,0.3699793008,0.2879466627,0.301826322,0.2937933689,1.0148176093,0.4535022053,0.5015055777,1.1157526247,0.5015055777,1.328999921,1.4420052382000002
-boomlet_1225,0.0982049933,0.0990409314,0.1000748977,0.0968533979,0.1144239787,0.1024577962,0.1066372427,0.1172112941,0.1451941336,0.12260998,0.1651551358,0.1711256418,0.3809038981,0.3809038981,0.3876340759
-boomlet_1230,0.2619779044,0.2653064283,0.2547608466,0.2477353188,0.3987332881,0.2744689053,0.3010213735,0.2814650284,0.9994018454,0.4477425115,228079607857629.03,1.1951987922,0.5006062673,1.7490874961,1.895555107
-boomlet_1282,0.2857642723,0.2813823066,0.2769507583,0.2786303196,0.292132349,0.2943955518,0.3174313196,0.3125554342,0.49773322,0.3750317971,0.62613098,0.6672579187000001,1.0473420209,1.0473420209,1.0675288199
-boomlet_1487,0.1664420355,0.1679334305,0.1621616582,0.1572610369,0.2936465116,0.179562264,0.190011281,0.1963999731,0.2664583485,0.2570055801,0.2835559204,0.3266562805,0.3287160259,2.1936503487,2.3761204352
-boomlet_1631,0.2500222003,0.2578011825,0.2521962154,0.2530865122,0.294595714,0.2594328079,0.2712471357,0.2762733263,0.3678343488,0.3678343488,0.3219452018,0.3246702609,0.3678343488,0.5939295732000001,0.6115095201
-boomlet_1676,0.2538630466,0.2548401001,0.2519960915,0.2495295993,0.2853155601,0.2579772293,0.2757684538,0.274422727,0.3899388202,0.3899388202,0.3561141453,0.3689668234,0.3899388202,0.5376397046,0.5530190574
-boomlet_1855,0.1371847152,0.1348268939,0.1418942616,0.1355059408,0.1913295293,0.139558043,0.1402605794,0.1563253928,0.2389634823,0.2316298817,0.2553622581,0.2591897672,0.3053456375,0.5272950774,0.5309525567
-boomlet_1975,0.0754861483,0.1095269694,0.0946619153,0.0726927455,0.1155713746,0.1195154441,0.1030843609,0.1103445012,0.3109341662,0.3101025558,0.3508947927,0.3645838608,0.490065287,0.3511931898,0.3522290761
-boomlet_2187,0.2318940307,0.2304085287,0.2575487452,0.24435445,0.2994495011,0.2578882698,0.2524757438,0.2800640842,0.4312238249,0.4412020452,0.4456954551,0.4740037125,0.5699897289,1.0418096521,1.049295268
-boomlet_285,0.1346407915,0.148143136,0.1683399577,0.1331450908,0.1553438735,0.1889532567,0.2234209762,0.2244579194,0.3030251372,0.2917086037,0.3012758228,0.3165758913,1.2032068551,1.2032068551,1.2258270453
-boomlet_619,0.2302330587,0.2471279568,0.2458341339,0.2162579975,0.2349216897,0.2356420104,0.3764453393,0.2649462131,0.6585898854000001,0.4527380943,0.7635945062,0.6993259862,0.9678549923,0.9678549923,0.9877621571
-boomlet_772,0.1331013781,0.136682574,0.1358824185,0.1307594429,0.1546550252,0.1437870275,0.155831971,0.1611776721,0.4436068427,0.3102148884,233223016.4792193,0.5422949313000001,0.9858607218,0.9858607218,1.0040136572
-boomlet_963,0.3166849612,0.3257730396,0.3291269599,0.3152013005,0.368648075,0.3420300574,0.3596501239,0.3456713575,0.6331985559000001,0.5019102045,0.7424657093,0.716591367,0.811345873,0.811345873,0.8256079501
-ecdc_ili,0.3096012763,0.3706244122,0.3434282741,0.3944662068,0.3232828815,0.3850819865,0.4167010575,0.4694310556,0.5427352164,0.5348657716,0.5818744542000001,0.5760490118,0.5547015104,0.5547015104,0.5751070381
-entsoe_15T,0.0332921923,0.03370606,0.0334792124,0.0413516905,0.0335205629,0.0343344442,0.0362665583,0.0499695365,0.0566124331,0.0566124331,0.313985467,0.04302748,0.0566124331,0.1154915649,0.1262425683
-entsoe_1H,0.0261903898,0.0293722588,0.029016943,0.0302241335,0.0272967255,0.033285571,0.0281751907,0.050980506,0.0660357719,0.0666871832,0.1446894285,0.0693891454,0.0798215745,0.144830171,0.1529602098
-entsoe_30T,0.029067509,0.0314140317,0.0388367365,0.0301774471,0.0321818804,0.0318440586,0.0321248654,0.0485585461,0.0612238614,0.0731621073,0.1815717143,0.0571609073,0.0725922166,0.1142494542,0.1132384606
-epf_be,0.0901568483,0.0962218885,0.0904647786,0.1057693667,0.0932975438,0.0968269743,0.106932535,0.116974555,0.2109061816,0.1868416274,0.2658805243,0.2582438044,0.2012625857,0.5357212884,0.5396597512
-epf_de,0.24071768,0.468452292,0.4775486473,0.5515425934,0.2426255873,0.4585770885,0.4643713196,0.5102784096,0.4882154408,0.5432797572,0.5287818089,0.6490481061000001,0.5919144005,0.5286529239000001,0.5344012453
-epf_fr,0.0548184681,0.063925129,0.0640304463,0.0682821872,0.0491162423,0.0646047834,0.0684544774,0.0719692742,0.1676570118,0.171487674,0.1386675645,0.2296495476,0.1818024008,0.5500836774,0.5542629353
-epf_np,0.0297236499,0.0438147562,0.0519221,0.0471320493,0.0293296573,0.0419786745,0.0445199614,0.0428252373,0.05738593,0.0636813178,0.086372811,0.0578512541,0.0691889058,0.0868798682,0.0869585173
-epf_pjm,0.0667748898,0.071403604,0.0757086151,0.0799833604,0.0739926826,0.0766169612,0.0739331273,0.084093308,0.0862614181,0.0855296797,0.1606961463,0.1068456977,0.0908421958,0.1641666584,0.1656245552
-ercot_1D,0.0597422214,0.0583644776,0.0590120341,0.0621409451,0.0688811199,0.0637608291,0.0620146905,0.0665392831,0.0936050684,0.0865494548,0.1027502478,0.1055339552,0.1044921679,0.1043182678,0.1051661229
-ercot_1H,0.0517757195,0.0542294904,0.0584985426,0.0555864688,0.0615615188,0.0563341027,0.0597259069,0.0625128045,0.0642267414,0.0597358018,0.1494485063,0.0646341807,0.0682466944,0.1331050238,0.1354763369
-ercot_1M,0.0374032871,0.0419160577,0.0380629909,0.0530588211,0.0407906509,0.0509930124,0.0398626935,0.0434627402,0.0375336712,0.0388325707,0.0371511279,0.0470765052,0.0427842586,0.2106942527,0.2307212042
-ercot_1W,0.046153037,0.0457429263,0.0465659059,0.0515355854,0.0531508617,0.0502061941,0.0467287195,0.0583864951,0.1138242996,0.1129826874,0.1134717335,0.1163159858,0.1143459151,0.1143459151,0.1147730701
-favorita_stores_1D,0.1111436033,0.1242660249,0.1184216613,0.1439165317,0.1204039869,0.1278709711,0.1418629323,0.1353609503,0.1606265395,0.1647292421,0.1672181851,0.1778642628,0.2375760789,0.4079240646,0.4110111744
-favorita_stores_1M,0.1214689726,0.1421527991,0.1508910378,0.1391907522,0.0943255732,0.1725604577,0.1852146109,0.2222006124,0.1265354908,0.1314480379,0.1328922997,0.1244588255,0.1715121001,0.1701131599,0.1843808726
-favorita_stores_1W,0.108858649,0.1134128786,0.109472936,0.1223103152,0.1011036417,0.1243048273,0.1268154753,0.1312294599,0.1336292959,0.1331314369,0.1429768358,0.1407957933,0.1608274596,0.1608274596,0.1626164075
-favorita_transactions_1D,0.0493905048,0.067704191,0.0735763953,0.0735763953,0.0603391606,0.0735763953,0.0735763953,0.0710714949,0.094900864,0.1055051904,0.0944759338,0.0991455646,0.1403339153,0.2372471038,0.2388998485
-favorita_transactions_1M,0.0604582877,0.0683964364,0.0642577109,0.0770716759,0.0718016091,0.0756335151,0.0799691906,0.0834713904,0.0705845865,0.0776326578,0.0709936943,0.0829960463,0.0855815678,0.1052862092,0.1152703332
-favorita_transactions_1W,0.0471234505,0.049259837,0.0528246684,0.0575771001,0.0575076503,0.0569446098,0.0528246684,0.0632163955,0.0648065086,0.0648478224,0.0694461916,0.0703094331,0.0783787891,0.0783787891,0.080280481
-fred_md_2025/cee,0.0668234044,0.0670834958,0.0750783838,0.0750783838,0.0743757483,0.0750783838,0.0750783838,0.0954701869,0.1051946757,0.133660425,0.0757646768,0.1182489158,0.2073879582,0.1174362958,0.1155920484
-fred_md_2025/macro,0.0658448953,0.0651736736,0.0689005701,0.0689005701,0.0703198143,0.0689005701,0.0689005701,0.0758142866,0.0780002055,0.0859787221,0.0754502178,0.0815239103,0.119592083,0.0816199937,0.0813235213
-fred_qd_2025/cee,0.1349362833,0.1205702323,0.1290291477,0.1111966207,0.1232875729,0.1271824649,0.1238446163,0.1631968012,0.1373877839,0.1474873424,0.1365938852,0.1482369083,0.194192155,0.1537726616,0.1492650928
-fred_qd_2025/macro,0.087749288,0.0847291191,0.0852353396,0.0836177008,0.0926080385,0.0863520335,0.0870314598,0.1012011908,0.0918310561,0.0955504799,0.0949233782,0.0954220424,0.1198022215,0.0963745296,0.0950284899
-gvar,0.0149820688,0.0150792847,0.0152665936,0.0149166365,0.0179419087,0.015061142,0.015482776,0.0185296964,0.0162937715,0.0169170494,0.0159433726,0.0166324554,0.0212211567,0.0176762713,0.0174836229
-hermes,0.00225818,0.0024187939,0.0022928048,0.0036102677,0.0026219625,0.0026712263,0.0025240351,0.0031453384,0.0061269301,0.0050407278,0.0073879456,0.0068258834,0.0087083111,0.0087083111,0.0090940295
-hierarchical_sales_1D,0.5714810084,0.5556084899,0.5615462267,0.5599444088000001,0.5928670205000001,0.5634020932,0.5634020932,0.647815962,0.8177303567,0.6915383974,0.9358165942,0.9689367577,1.1222145629,1.6764214744,1.6911406182
-hierarchical_sales_1W,0.3648561948,0.3675267983,0.36900549,0.3702396077,0.3741749103,0.3762527696,0.3762527696,0.4228486488,0.465201591,0.4672185482,7.8275973261,0.4883602498,0.9260072924,0.9260072924,0.9465583798
-hospital,0.0748078223,0.0726720337,0.0714240645,0.0782312026,0.0744330876,0.075323756,0.075323756,0.0900986355,0.0749195627,0.0845424144,0.0759570393,0.0813819141,0.0783920641,0.1042062258,0.1105393325
-hospital_admissions_1D,0.4112482559,0.4117757474,0.4124642645,0.4120033633,0.4170562141,0.4120897736,0.4125280894,0.4526293478,0.41312756,0.4121681663,0.4122712942,0.4263733391,0.6361459769000001,0.9837276521,0.9916738218
-hospital_admissions_1W,0.1624167733,0.1647817222,0.1632100946,0.1680866908,0.163762699,0.1651659256,0.1652841678,0.1802484199,0.1630497452,0.1631143081,0.1628325575,0.168270042,0.2969374559,0.2969374559,0.3078008055
-jena_weather_10T,0.2528507582,0.2661828374,0.2127668736,0.2353999499,0.2551716964,0.2625552838,0.2625552838,0.3371179261,1.7057621919,1.7057621919,3.1495387185,3.5724878985,1.7057621919,3.6828586054,3.8552910899
-jena_weather_1D,0.2257062567,0.2210204491,0.218506197,0.2246469289,0.2320364869,0.2254046561,0.2254046561,0.2665635899,0.3084284928,0.3017046194,0.3511726073,0.352949988,0.4719762386,0.6992484433,0.7361988018000001
-jena_weather_1H,0.2250019726,0.2168159034,0.2233014541,0.2068719624,0.2343045768,0.234099159,0.234099159,0.4350680693,1.0549343053,0.8256929905,1.4084504179,1.6859107034,1.2878361856,1.6601789319,1.6730314795
-kdd_cup_2022_10T,1.2012691577,1.3753457151,1.3753457151,1.3753457151,2.3282104591,1.3753457151,1.3753457151,1.3753457151,3.4658735242,3.4658735242,2.5986419348,2.5543058657,3.4658735242,2.6760920983,2.9246115818
-kdd_cup_2022_1D,0.5599962178,0.556621323,0.5527344226,0.5570149360000001,0.5680073145,0.5630864114,0.5631888802,0.6469029607,0.5920639476,0.5843812529,0.6089510673,0.5966451508,0.75069981,0.9265198159,0.948426295
-kdd_cup_2022_30T,1.3117734404,1.3401124353,1.6757596515,1.3756596821,2.2207570053,1.2046794654,2.2656435299,1.6183676077,2.2310513851,2.1850967479,2.6468743604,2.5972049691,3.2580293089,2.5790907336,2.6373925567
-m5_1D,0.5566648803,0.5522750547,0.5619946767,0.5619946767,0.8588793502000001,0.5619946767,0.5619946767,0.6369837787,0.8588793502000001,0.611489809,0.6156373922,0.6265285781000001,0.8588793502000001,1.352981474,1.3656864863
-m5_1M,0.3477280487,0.3446529974,0.3370295405,0.3648378977,0.3466619129,0.3456589364,0.3566383823,0.3886462021,0.3701215676,0.380094291,0.4320692413,0.4195249159,0.4285745193,0.4471596243,0.5002848742
-m5_1W,0.3375480227,0.3359465585,0.3409590155,0.3368808055,0.3436698517,0.3361267514,0.3409590155,0.3622685986,0.3617147564,0.3580006782,0.3728147004,0.3767071031,0.5198944018,0.5198944018,0.5321822364000001
-proenfo_gfc12,0.0555966984,0.0771150803,0.077263494,0.077263494,0.0702222225,0.077263494,0.077263494,0.0732963269,0.1181658339,0.0987456528,0.2072982242,0.1250899583,0.1088404134,0.2075379042,0.2175193024
-proenfo_gfc14,0.0210605981,0.0353901819,0.0375513457,0.0375513457,0.0252054415,0.0375513457,0.0375513457,0.0204786531,0.0444124312,0.0464290142,0.0544938011,0.0519159577,0.0529491741,0.158759622,0.1683589627
-proenfo_gfc17,0.0315349181,0.0597007945,0.0608284875,0.0608284875,0.0440642892,0.0608284875,0.0608284875,0.0315794369,0.0769991028,0.0761960672,0.146648178,0.0769725348,0.0897222269,0.1691034077,0.1777795871
-redset_15T,0.2174028754,0.2431114555,0.2501147518,0.2510706717,0.2325884637,0.2504748238,0.2713077355,0.2914044606,0.4607365658,0.4607365658,0.4607365658,0.940375013,0.4607365658,1.565727146,1.6075485006
-redset_1H,0.1754538498,0.1792595698,0.1953408446,0.1912104253,0.1873583276,0.1892621749,0.2027229088,0.2287683337,0.3644260201,0.347358315,3.3022819464,0.471399676,0.4120467256,0.8881015738,0.8935980201
-redset_5T,0.2706931762,0.3109114114,0.288140782,0.2789818321,0.289831124,0.3116496574,0.3706331476,0.3372367613,0.9331343204,0.6814341656,0.5026322375,1.1479851264,0.5026322375,2.8198686088,3.0482376442
-restaurant,0.2820261417,0.282683528,0.2808265082,0.2932229182,0.286941911,0.2874369433,0.2874369433,0.3121784735,0.2987534022,0.3174475777,0.4313426382,0.3228884373,0.4263793897,0.7194392994000001,0.7547545082
-rohlik_orders_1D,0.0453576141,0.046778087,0.0473405217,0.0531957347,0.0585028628,0.0454924381,0.0495487283,0.0570594271,0.0551848097,0.0603755941,0.0637016666,0.0607226711,0.0730389024,0.1384185633,0.141017357
-rohlik_orders_1W,0.0419834034,0.0425926735,0.0430425121,0.0489199152,0.049953361,0.0500948995,0.047050427,0.0608989622,0.0465205009,0.0471291762,0.046928104,0.0464560362,0.0498648091,0.0498648091,0.0497673655
-rohlik_sales_1D,0.2203668325,0.3154378947,0.2983877224,0.3351760018,0.3727953997,0.3202051989,0.3139840107,0.3250144355,0.3425447103,0.3459201995,0.3543382966,0.3563233504,0.3727953997,0.4099413728,0.4141182186
-rohlik_sales_1W,0.1860678461,0.2252347071,0.2196835891,0.2348251568,0.1697896851,0.2364913465,0.2336631822,0.2594374504,0.2595910509,0.2670259076,4.0491052559,0.2655509054,0.3323396817,0.3323396817,0.3393924276
-rossmann_1D,0.0954525576,0.1837160416,0.171116383,0.1931728074,0.0772288962,0.1799313223,0.178806336,0.1802362487,0.1969163908,0.1908416644,0.2028079674,0.291009868,0.3139894604,0.93885619,0.9579696488
-rossmann_1W,0.08024438,0.1304017962,0.1350575869,0.1345092339,0.0659741153,0.1342178268,0.1317076013,0.1572071227,0.135127457,0.1410069966,0.1401558708,0.1385267381,0.2508975252,0.2508975252,0.2680805063
-solar_1D,0.1855480731,0.1929553199,0.1938342095,0.1955625549,0.1919496514,0.2003849004,0.1990286403,0.2094179217,0.2041438251,0.2053532934,0.2049319678,0.2116725603,0.2794918566,0.4319792148,0.4569554917
-solar_1W,0.1513634778,0.1885658631,0.1848997811,0.2335483112,0.1459644443,0.2786290198,0.1573930974,0.1567824467,0.2181202588,0.2154878912,0.2063617654,0.2389863753,0.241539109,0.241539109,0.3053960241
-solar_with_weather_15T,0.9550528101,1.2067686983,1.2404817956,0.9998316485,0.9712352474,1.1204703544,1.1300785408,1.3507714502,1.4073798197,1.4073798197,1.9572357231,3.2872524251,1.4073798197,1.6964307303,1.7761051768
-solar_with_weather_1H,0.9780600229,1.2498913502,0.9695951652,1.2191318813,0.7960101453,1.2285296788,1.0189950233,1.3598337832,1.1487515083,1.2702665577,1.3928226357,2.6714879984,1.3170887607,1.3916210721,1.3968565452
-uci_air_quality_1D,0.2146017914,0.2321704936,0.2425060369,0.2553475038,0.239766452,0.2299092189,0.2228595882,0.2497182101,0.2303977966,0.2530270906,0.2425004358,0.2579293608,0.2922307509,0.3990393174,0.4166124807
-uci_air_quality_1H,0.2626699279,0.2890236922,0.2903775779,0.2863033352,0.3066820684,0.3062706804,0.3020104526,0.3368853083,0.5268864008,0.3923535075,20468.2757052641,0.6724046824000001,0.460867968,0.8360828268,0.8759093056
-uk_covid_nation_1D/cumulative,0.0161921475,0.0138342964,0.0153292336,0.0125938684,0.0316628726,0.0145084122,0.0190395107,0.0397848581,0.0188346391,0.0173381483,0.0163459748,0.0435228041,0.0616683882,0.0540829702,0.0420838658
-uk_covid_nation_1D/new,0.3207938344,0.3109536889,0.3156210831,0.2838323856,0.3652919167,0.295667223,0.3374595917,0.3704647201,0.6131520742000001,0.6717556157,0.7010568349,0.6249699219,0.7590347607,0.6807093543,0.6959726061
-uk_covid_nation_1W/cumulative,0.0231629135,0.0306531397,0.0334625898,0.0259156466,0.0313019374,0.0235252689,0.0308278174,0.0470469389,0.0198408228,0.0621980203,0.0195175599,0.0457146098,0.0708838512,0.0708838512,0.0450304907
-uk_covid_nation_1W/new,0.577389366,0.5056475261,0.3916377081,0.5874941513,0.5181874025000001,0.4243048101,0.4123778016,0.346954435,0.4963902021,0.7570660296,0.4600308825,0.4631006396,0.4524501242,0.4524501242,0.4739863292
-uk_covid_utla_1D/new,0.3910187501,0.3797951228,0.3533112754,0.4097196009,0.3768120784,0.3538288145,0.3616969038,0.3594274377,0.5001590872,0.6134348659000001,0.5094112038,0.476165342,0.4420318364,0.5157896779,0.5265475783
-uk_covid_utla_1W/cumulative,0.1710838548,0.2003527874,0.1901138083,0.1555521636,0.16972636,0.195409151,0.1670758111,0.2660261673,0.138335546,0.1351972799,0.1685579648,0.186827715,0.2340614394,0.2340614394,0.1835390955
-us_consumption_1M,0.0197271602,0.0197402218,0.0226701155,0.0205041485,0.0243746544,0.0218430056,0.0218589731,0.032429082,0.0214320964,0.0239150894,0.0201648123,0.0272716281,0.0497079002,0.032661543,0.0266511556
-us_consumption_1Q,0.0274404821,0.0279758108,0.026686332,0.0270793373,0.039451329,0.0274257255,0.0277102453,0.050684032,0.0294259535,0.030008464,0.0292799577,0.0412327495,0.0619774434,0.0509220091,0.0386050624
-us_consumption_1Y,0.049885574,0.0438966964,0.0465564099,0.0546159459,0.0536317719,0.0851402532,0.0656167649,0.0997271328,0.0515666785,0.0428692104,0.0592817405,0.0967727372,0.1514273794,0.1514273794,0.0943820837
-walmart,0.0739271756,0.0849769317,0.0803039051,0.113539905,0.0751515146,0.1057545178,0.0950021434,0.1036346439,0.1643578174,0.1319843981,119300400139.46916,0.1996789224,0.3075011099,0.3075011099,0.3407542314
-world_co2_emissions,0.0729773135,0.0707450544,0.0751862129,0.0712556895,0.0717195922,0.076153015,0.0727688426,0.0955702861,0.0701479592,0.0705282623,0.0887646329,0.0742405541,0.0832284265,0.0832284265,0.073077502
-world_life_expectancy,0.0095948731,0.0089847076,0.0097525655,0.0120911181,0.0093362704,0.0144690768,0.0108645301,0.0129798805,0.010866463,0.0112841979,0.0107918452,0.0118279018,0.0143077928,0.0143077928,0.0130987043
-world_tourism,0.0908975708,0.0849997606,0.0984825726,0.0868200211,0.0601786476,0.0894951758,0.0889419278,0.1403522964,0.0552036802,0.0578924108,0.0724923711,0.0479228397,0.0866853455,0.0866853455,0.0439170643

tables/pivot_WQL_baseline_imputed.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TiRex,TimesFM-2.5,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
-ETT_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-ETT_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ETT_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ETT_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1H,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-LOOP_SEATTLE_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-M_DENSE_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-SZ_TAXI_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-SZ_TAXI_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1209,False,False,False,False,False,False,False,False,False,False,True,False,False,False,False
-boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1282,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1631,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-boomlet_1676,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_619,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_10T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-jena_weather_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_10T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1D,False,False,False,False,True,False,False,False,True,False,False,False,False,False,False
-m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc12,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc14,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc17,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_15T,False,False,False,False,False,False,False,False,True,True,True,False,False,False,False
-redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_5T,False,False,False,False,False,False,False,False,False,False,True,False,False,False,False
-restaurant,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_15T,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False
-solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_WQL_leakage_imputed.csv

@@ -1,101 +0,0 @@
-Task name,Chronos-2,TiRex,TimesFM-2.5,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
-ETT_15T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-ETT_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-ETT_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-ETT_1W,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-LOOP_SEATTLE_5T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-M_DENSE_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-M_DENSE_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-SZ_TAXI_15T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-SZ_TAXI_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-bizitobs_l2c_5T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1209,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1282,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1631,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1676,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_619,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1D,False,False,True,True,False,True,False,False,False,False,False,False,False,False,False
-favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-favorita_transactions_1W,False,False,True,False,False,False,False,False,False,False,False,False,False,False,False
-fred_md_2025/cee,False,False,True,True,False,True,False,False,False,False,False,False,False,False,False
-fred_md_2025/macro,False,False,True,True,False,True,False,False,False,False,False,False,False,False,False
-fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-hierarchical_sales_1W,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-hospital,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-jena_weather_10T,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-jena_weather_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-jena_weather_1H,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_10T,False,True,True,True,False,True,False,True,False,False,False,False,False,False,False
-kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1D,False,False,True,True,False,True,False,False,False,False,False,False,False,False,False
-m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-m5_1W,False,False,True,False,False,False,False,False,False,False,False,False,False,False,False
-proenfo_gfc12,False,False,True,True,False,True,False,False,False,False,False,False,False,False,False
-proenfo_gfc14,False,False,True,True,False,True,False,False,False,False,False,False,False,False,False
-proenfo_gfc17,False,False,True,True,False,True,False,False,False,False,False,False,False,False,False
-redset_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-redset_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-restaurant,False,False,False,False,False,True,False,False,False,False,False,False,False,False,False
-rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False
-world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False