class_docstr: Forecasting with an Complex Exponential Smoothing model where the model selection is performed using the
  Akaike Information Criterion.
  
  Based on `statsforecast.models.AutoCES <https://nixtla.mintlify.app/statsforecast/docs/models/autoces.html>`_.
  
  
  References
  ----------
  .. [Svetunkov2022] Svetunkov, Ivan, Nikolaos Kourentzes, and John Keith Ord. "Complex exponential
      smoothing." Naval Research Logistics (NRL) 69.8 (2022): 1108-1123.
  
  
  Other Parameters
  ----------------
  model : {"Z", "N", "S", "P", "F"}, default = "Z"
      Defines type of CES model, "N" for simple CES, "S" for simple seasonality, "P" for partial seasonality
      (without complex part), "F" for full seasonality. When "Z" is selected, the best model is selected using
      Akaike Information Criterion (AIC).
  seasonal_period : int or None, default = None
      Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a
      weekly cycle or 12 for monthly data with an annual cycle.
      When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be
      specified manually by providing an integer > 1.
      If seasonal_period (inferred or provided) is equal to 1, seasonality will be disabled.
  n_jobs : int or float, default = 0.5
      Number of CPU cores used to fit the models in parallel.
      When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used.
      When set to a positive integer, that many cores are used.
      When set to -1, all CPU cores are used.
  max_ts_length : int, default = 2500
      If not None, only the last ``max_ts_length`` time steps of each time series will be used to train the model.
      This significantly speeds up fitting and usually leads to no change in accuracy.
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code: def _update_local_model_args(self, local_model_args: dict) -> dict:
      local_model_args = super()._update_local_model_args(
          local_model_args
      )
      local_model_args.setdefault("model", "Z")
      return local_model_args
  
imports: import logging
  from typing import Any, Dict, Type
  import numpy
  import pandas
  from .abstract_local_model import AbstractLocalModel
label_desc: Use image node_id 2 for calling the AutoCESModel obj's underlying member method code with example usage: obj._update_local_model_args(local_model_args) and returns: local_model_args