class_docstr: NeuralProphet forecaster.
  
  A simple yet powerful forecaster that models:
  Trend, seasonality, events, holidays, auto-regression, lagged covariates, and future-known regressors.
  Can be regularized and configured to model nonlinear relationships.
  
  Parameters
  ----------
      COMMENT
      Trend Config
      COMMENT
      growth : {'off' or 'linear'}, default 'linear'
          Set use of trend growth type.
  
          Options:
              * ``off``: no trend.
              * (default) ``linear``: fits a piece-wise linear trend with ``n_changepoints + 1`` segments
              * ``discontinuous``: For advanced users only - not a conventional trend,
              allows arbitrary jumps at each trend changepoint
  
      changepoints : {list of str, list of np.datetimes or np.array of np.datetimes}, optional
          Manually set dates at which to include potential changepoints.
  
          Note
          ----
          Does not accept ``np.array`` of ``np.str``. If not specified, potential changepoints are selected
          automatically.
  
      n_changepoints : int
          Number of potential trend changepoints to include.
  
          Note
          ----
          Changepoints are selected uniformly from the first ``changepoint_range`` proportion of the history.
          Ignored if manual ``changepoints`` list is supplied.
      changepoints_range : float
          Proportion of history in which trend changepoints will be estimated.
  
          e.g. set to 0.8 to allow changepoints only in the first 80% of training data.
          Ignored if  manual ``changepoints`` list is supplied.
      trend_reg : float, optional
          Parameter modulating the flexibility of the automatic changepoint selection.
  
          Note
          ----
          Large values (~1-100) will limit the variability of changepoints.
          Small values (~0.001-1.0) will allow changepoints to change faster.
          default: 0 will fully fit a trend to each segment.
  
      trend_reg_threshold : bool, optional
          Allowance for trend to change without regularization.
  
          Options
              * ``True``: Automatically set to a value that leads to a smooth trend.
              * (default) ``False``: All changes in changepoints are regularized
  
      trend_global_local : str, default 'global'
          Modelling strategy of the trend when multiple time series are present.
  
          Options:
              * ``global``: All the elements are modelled with the same trend.
              * ``local``: Each element is modelled with a different trend.
  
          Note
          ----
          When only one time series is input, this parameter should not be provided.
          Internally it will be set to ``global``, meaning that all the elements(only one in this case)
          are modelled with the same trend.
  
      COMMENT
      Seasonality Config
      COMMENT
      yearly_seasonality : bool, int
          Fit yearly seasonality.
  
          Options
              * ``True`` or ``False``
              * ``auto``: set automatically
              * ``value``: number of Fourier/linear terms to generate
      weekly_seasonality : bool, int
          Fit monthly seasonality.
  
          Options
              * ``True`` or ``False``
              * ``auto``: set automatically
              * ``value``: number of Fourier/linear terms to generate
      daily_seasonality : bool, int
          Fit daily seasonality.
  
          Options
              * ``True`` or ``False``
              * ``auto``: set automatically
              * ``value``: number of Fourier/linear terms to generate
      seasonality_mode : str
          Specifies mode of seasonality
  
          Options
              * (default) ``additive``
              * ``multiplicative``
      seasonality_reg : float, optional
          Parameter modulating the strength of the seasonality model.
  
          Note
          ----
          Smaller values (~0.1-1) allow the model to fit larger seasonal fluctuations,
          larger values (~1-100) dampen the seasonality.
          default: None, no regularization
      season_global_local : str, default 'global'
          Modelling strategy of the seasonality when multiple time series are present.
          Options:
              * ``global``: All the elements are modelled with the same seasonality.
              * ``local``: Each element is modelled with a different seasonality.
          Note
          ----
          When only one time series is input, this parameter should not be provided.
          Internally it will be set to ``global``, meaning that all the elements(only one in this case)
          are modelled with the same seasonality.
  
      COMMENT
      AR Config
      COMMENT
      n_lags : int
          Previous time series steps to include in auto-regression. Aka AR-order
      ar_reg : float, optional
          how much sparsity to induce in the AR-coefficients
  
          Note
          ----
          Large values (~1-100) will limit the number of nonzero coefficients dramatically.
          Small values (~0.001-1.0) will allow more non-zero coefficients.
          default: 0 no regularization of coefficients.
      ar_layers : list of int, optional
          array of hidden layer dimensions of the AR-Net. Specifies number of hidden layers (number of entries)
          and layer dimension (list entry).
  
      COMMENT
      Model Config
      COMMENT
      n_forecasts : int
          Number of steps ahead of prediction time step to forecast.
      lagged_reg_layers : list of int, optional
          array of hidden layer dimensions of the Covar-Net. Specifies number of hidden layers (number of entries)
          and layer dimension (list entry).
  
      COMMENT
      Train Config
      COMMENT
      learning_rate : float
          Maximum learning rate setting for 1cycle policy scheduler.
  
          Note
          ----
          Default ``None``: Automatically sets the ``learning_rate`` based on a learning rate range test.
          For manual user input, (try values ~0.001-10).
      epochs : int
          Number of epochs (complete iterations over dataset) to train model.
  
          Note
          ----
          Default ``None``: Automatically sets the number of epochs based on dataset size.
          For best results also leave batch_size to None. For manual values, try ~5-500.
      batch_size : int
          Number of samples per mini-batch.
  
          If not provided, ``batch_size`` is approximated based on dataset size.
          For manual values, try ~8-1024.
          For best results also leave ``epochs`` to ``None``.
      newer_samples_weight: float, default 2.0
          Sets factor by which the model fit is skewed towards more recent observations.
  
          Controls the factor by which final samples are weighted more compared to initial samples.
          Applies a positional weighting to each sample's loss value.
  
          e.g. ``newer_samples_weight = 2``: final samples are weighted twice as much as initial samples.
      newer_samples_start: float, default 0.0
          Sets beginning of 'newer' samples as fraction of training data.
  
          Throughout the range of 'newer' samples, the weight is increased
          from ``1.0/newer_samples_weight`` initially to 1.0 at the end,
          in a monotonously increasing function (cosine from pi to 2*pi).
      loss_func : str, torch.nn.functional.loss
          Type of loss to use:
  
          Options
              * (default) ``SmoothL1Loss``: SmoothL1 loss function
              * ``MSE``: Mean Squared Error loss function
              * ``MAE``: Mean Absolute Error loss function
              * ``torch.nn.functional.loss.``: loss or callable for custom loss, eg. L1-Loss
  
          Examples
          --------
          >>> from neuralprophet import NeuralProphet
          >>> import torch
          >>> import torch.nn as nn
          >>> m = NeuralProphet(loss_func=torch.nn.L1Loss)
  
      collect_metrics : list of str, dict, bool
          Set metrics to compute.
  
          Options
              * (default) ``True``: [``mae``, ``rmse``]
              * ``False``: No metrics
              * ``list``:  Valid options: [``mae``, ``rmse``, ``mse``]
              * ``dict``:  Collection of names of torchmetrics.Metric objects
  
          Examples
          --------
          >>> from neuralprophet import NeuralProphet
          >>> # computer MSE, MAE and RMSE
          >>> m = NeuralProphet(collect_metrics=["MSE", "MAE", "RMSE"])
          >>> # use custorm torchmetrics names
          >>> m = NeuralProphet(collect_metrics={"MAPE": "MeanAbsolutePercentageError", "MSLE": "MeanSquaredLogError",
  
      COMMENT
      Uncertainty Estimation
      COMMENT
      quantiles : list, default None
          A list of float values between (0, 1) which indicate the set of quantiles to be estimated.
  
      COMMENT
      Missing Data
      COMMENT
      impute_missing : bool
          whether to automatically impute missing dates/values
  
          Note
          ----
          imputation follows a linear method up to 20 missing values, more are filled with trend.
      impute_linear : int
          maximal number of missing dates/values to be imputed linearly (default: ``10``)
      impute_rolling : int
          maximal number of missing dates/values to be imputed
          using rolling average (default: ``10``)
      drop_missing : bool
          whether to automatically drop missing samples from the data
  
          Options
              * (default) ``False``: Samples containing NaN values are not dropped.
              * ``True``: Any sample containing at least one NaN value will be dropped.
  
      COMMENT
      Data Normalization
      COMMENT
      normalize : str
          Type of normalization to apply to the time series.
  
          Options
              * ``off`` bypasses data normalization
              * (default, binary timeseries) ``minmax`` scales the minimum value to 0.0 and the maximum value to 1.0
              * ``standardize`` zero-centers and divides by the standard deviation
              * (default) ``soft`` scales the minimum value to 0.0 and the 95th quantile to 1.0
              * ``soft1`` scales the minimum value to 0.1 and the 90th quantile to 0.9
      global_normalization : bool
          Activation of global normalization
  
          Options
              * ``True``: dict of dataframes is used as global_time_normalization
              * (default) ``False``: local normalization
      global_time_normalization : bool
          Specifies global time normalization
  
          Options
              * (default) ``True``: only valid in case of global modeling local normalization
              * ``False``: set time data_params locally
      unknown_data_normalization : bool
          Specifies unknown data normalization
  
          Options
              * ``True``: test data is normalized with global data params even if trained with local data params
              (global modeling with local normalization)
              * (default) ``False``: no global modeling with local normalization
      accelerator: str
          Name of accelerator from pytorch_lightning.accelerators to use for training. Use "auto" to automatically
          select an available accelerator.
          Provide `None` to deactivate the use of accelerators.
      trainer_config: dict
          Dictionary of additional trainer configuration parameters.
      prediction_frequency: dict
          periodic interval in which forecasts should be made.
          More than one item only allowed for {"daily-hour": x, "weekly-day": y"} to forecast on a specific hour of a
          specific day of week.
  
          Key: str
              periodicity of the predictions to be made.
          value: int
              forecast origin of the predictions to be made, e.g. 7 for 7am in case of 'daily-hour'.
  
          Options
              * ``'hourly-minute'``: forecast once per hour at a specified minute
              * ``'daily-hour'``: forecast once per day at a specified hour
              * ``'weekly-day'``: forecast once per week at a specified day
              * ``'monthly-day'``: forecast once per month at a specified day
              * ``'yearly-month'``: forecast once per year at a specified month
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docstr: Runs the model to make predictions.
  
  Predictions are returned in raw vector format without decomposition.
  Predictions are given on a forecast origin basis, not on a target basis.
  
  Parameters
  ----------
      df : pd.DataFrame
          dataframe containing column ``ds``, ``y``, and optionally``ID`` with all data
      df_name : str
          name of the data params from which the current dataframe refers to (only in case of local_normalization)
      include_components : bool
          whether to return individual components of forecast
  prediction_frequency: dict
      periodic interval in which forecasts should be made.
      Key: str
          periodicity of the predictions to be made, e.g. 'daily-hour'.
  
      Options
          * ``'hourly-minute'``: forecast once per hour at a specified minute
          * ``'daily-hour'``: forecast once per day at a specified hour
          * ``'weekly-day'``: forecast once per week at a specified day
          * ``'monthly-day'``: forecast once per month at a specified day
          * ``'yearly-month'``: forecast once per year at a specified month
  
      value: int
          forecast origin of the predictions to be made, e.g. 7 for 7am in case of 'daily-hour'.
  
  Returns
  -------
      pd.Series
          timestamps referring to the start of the predictions.
      np.array
          array containing the forecasts
      dict[np.array]
          Dictionary of components containing an array of each components contribution to the forecast
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code: def _predict_raw(
      self,
      df,
      df_name,
      include_components=False,
      prediction_frequency=None,
  ):
      """Runs the model to make predictions.
  
      Predictions are returned in raw vector format without decomposition.
      Predictions are given on a forecast origin basis, not on a target basis.
  
      Parameters
      ----------
          df : pd.DataFrame
              dataframe containing column ``ds``, ``y``, and optionally``ID`` with all data
          df_name : str
              name of the data params from which the current dataframe refers to (only in case of local_normalization)
          include_components : bool
              whether to return individual components of forecast
      prediction_frequency: dict
          periodic interval in which forecasts should be made.
          Key: str
              periodicity of the predictions to be made, e.g. 'daily-hour'.
  
          Options
              * ``'hourly-minute'``: forecast once per hour at a specified minute
              * ``'daily-hour'``: forecast once per day at a specified hour
              * ``'weekly-day'``: forecast once per week at a specified day
              * ``'monthly-day'``: forecast once per month at a specified day
              * ``'yearly-month'``: forecast once per year at a specified month
  
          value: int
              forecast origin of the predictions to be made, e.g. 7 for 7am in case of 'daily-hour'.
  
      Returns
      -------
          pd.Series
              timestamps referring to the start of the predictions.
          np.array
              array containing the forecasts
          dict[np.array]
              Dictionary of components containing an array of each components contribution to the forecast
      """
      # Receives df with single ID column
      assert len(df["ID"].unique()) == 1
      if "y_scaled" not in df.columns or "t" not in df.columns:
          raise ValueError(
              "Received unprepared dataframe to predict. "
              "Please call predict_dataframe_to_predict."
          )
      dataset = _create_dataset(
          self,
          df,
          predict_mode=True,
          prediction_frequency=prediction_frequency,
      )
      loader = DataLoader(
          dataset,
          batch_size=min(1024, len(df)),
          shuffle=False,
          drop_last=False,
      )
      if self.n_forecasts > 1:
          dates = df["ds"].iloc[self.max_lags : -self.n_forecasts + 1]
      else:
          dates = df["ds"].iloc[self.max_lags :]
  
      # Pass the include_components flag to the model
      if include_components:
          self.model.set_compute_components(include_components)
          self.model.set_covar_weights(self.model.get_covar_weights())
      # Compute the predictions and components (if requested)
      result = self.trainer.predict(self.model, loader)
      # Extract the prediction and components
      predicted, component_vectors = zip(*result)
      predicted = np.concatenate(predicted)
  
      # Post-process and normalize the predictions
      data_params = self.config_normalization.get_data_params(df_name)
      scale_y, shift_y = data_params["y"].scale, data_params["y"].shift
      predicted = predicted * scale_y + shift_y
  
      if include_components:
          component_keys = component_vectors[0].keys()
          components = {key: None for key in component_keys}
          # Transform the components list into a dictionary
          for batch in component_vectors:
              for key in component_keys:
                  components[key] = (
                      np.concatenate([components[key], batch[key]])  # type: ignore
                      if (components[key] is not None)
                      else batch[key]
                  )
          for name, value in components.items():
              multiplicative = (
                  False  # Flag for multiplicative components
              )
              if "trend" in name:
                  trend = value
              elif (
                  "event_" in name or "events_" in name
              ):  # accounts for events and holidays
                  event_name = name.split("_")[1]
                  if (
                      self.config_events is not None
                      and event_name in self.config_events
                  ):
                      if (
                          self.config_events[event_name].mode
                          == "multiplicative"
                      ):
                          multiplicative = True
                  elif (
                      self.config_country_holidays is not None
                      and event_name
                      in self.config_country_holidays.holiday_names
                  ):
                      if (
                          self.config_country_holidays.mode
                          == "multiplicative"
                      ):
                          multiplicative = True
                  elif "multiplicative" in name:
                      multiplicative = True
              elif (
                  "season" in name
                  and self.config_seasonality.mode == "multiplicative"
              ):
                  multiplicative = True
              elif (
                  "future_regressor_" in name
                  or "future_regressors_" in name
              ) and self.config_regressors is not None:
                  regressor_name = name.split("_")[2]
                  if (
                      self.config_regressors is not None
                      and regressor_name in self.config_regressors
                  ):
                      if (
                          self.config_regressors[regressor_name].mode
                          == "multiplicative"
                      ):
                          multiplicative = True
                  elif "multiplicative" in regressor_name:
                      multiplicative = True
  
              # scale additive components
              if not multiplicative:
                  components[name] = value * scale_y
                  if "trend" in name:
                      components[name] += shift_y
              # scale multiplicative components
              elif multiplicative:
                  # output absolute value of respective additive component
                  components[name] = value * trend * scale_y  # type: ignore
  
      else:
          components = None
  
      return dates, predicted, components
  
imports: import logging
  import os
  import time
  from collections import OrderedDict
  from typing import Callable, List, Optional, Tuple, Type, Union
  import matplotlib
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  from matplotlib import pyplot
  from matplotlib.axes import Axes
  from torch.utils.data import DataLoader
  from neuralprophet import configure, df_utils, np_types, time_dataset, time_net, utils, utils_metrics
  from neuralprophet.data.process import _check_dataframe, _convert_raw_predictions_to_raw_df, _create_dataset, _handle_missing_data, _prepare_dataframe_to_predict, _reshape_raw_predictions_to_forecst_df, _validate_column_name
  from neuralprophet.data.split import _make_future_dataframe, _maybe_extend_df
  from neuralprophet.data.transform import _normalize
  from neuralprophet.logger import MetricsLogger
  from neuralprophet.plot_forecast_matplotlib import plot, plot_components
  from neuralprophet.plot_forecast_plotly import conformal_plot_plotly
  from neuralprophet.plot_forecast_plotly import plot
  from neuralprophet.plot_forecast_plotly import plot_components
  from neuralprophet.plot_model_parameters_matplotlib import plot_parameters
  from neuralprophet.plot_model_parameters_plotly import plot_parameters
  from neuralprophet.plot_utils import get_valid_configuration, log_warning_deprecation_plotly, select_plotting_backend
  from neuralprophet.uncertainty import Conformal
label_desc: Use image node_id 35 for calling the NeuralProphet obj's underlying member method code with example usage: obj._predict_raw(df, df_name, include_components, prediction_frequency) and returns: dates,  predicted,  components