class_docstr: Exponential smoothing with trend and seasonality.
  
  The E (error), T (trend) and S (seasonal) components are fixed and provided by the user.
  
  This is an alias for `statsforecast.models.AutoETS <https://nixtla.mintlify.app/statsforecast/docs/models/autoets.html>`_.
  
  Other Parameters
  ----------------
  model : str, default = "AAA"
      Model string describing the configuration of the E (error), T (trend) and S (seasonal) model components.
      Each component can be one of "M" (multiplicative), "A" (additive), "N" (omitted). For example when model="ANN"
      (additive error, no trend, and no seasonality), ETS will explore only a simple exponential smoothing.
  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.
  damped : bool, default = False
      Whether to dampen the trend.
  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", "AAA")
      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 1 for calling the ETSModel obj's underlying member method code with example usage: obj._update_local_model_args(local_model_args) and returns: local_model_args