class_docstr: StatsForecast Complex Exponential Smoothing model.
  
  Direct interface to ``statsforecast.models.AutoCES``,
  from ``statsforecast`` [1]_ by Nixtla.
  
  Automatically selects the best Complex Exponential Smoothing model using an
  information criterion. Default is Akaike Information Criterion (AICc), while
  particular models are estimated using maximum likelihood. The state-space equations
  can be determined based on their $S$ simple, $P$ partial, $Z$ optimized or $N$
  omitted components. The `model` string parameter defines the kind of CES model:
  $N$ for simple CES (without seasonality), $S$ for simple seasonality (lagged CES),
  $P$ for partial seasonality (without complex part), $F$ for full seasonality
  (lagged CES with real and complex seasonal parts).
  
  If the component is selected as 'Z', it operates as a placeholder to ask the
  AutoCES model to figure out the best parameter.
  
  Parameters
  ----------
  season_length : int
      Number of observations per unit of time. Ex: 24 Hourly data.
  model : str
      Controlling state-space-equations.
  
  References
  ----------
  .. [1] https://nixtla.github.io/statsforecast/models.html#autoces
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code: def _get_statsforecast_params(self):
      return {
          "season_length": self.season_length,
          "model": self.model,
      }
  
imports: from typing import Dict, List, Optional, Union
  from sktime.forecasting.base import BaseForecaster
  from sktime.forecasting.base.adapters._generalised_statsforecast import StatsForecastBackAdapter, _GeneralisedStatsForecastAdapter
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 3 for calling the StatsForecastAutoCES obj's underlying member method code with example usage: obj._get_statsforecast_params() and returns: dict