class_docstr: StatsForecast Automatic Exponential Smoothing model.
  
  Direct interface to ``statsforecast.models.AutoETS``,
  from ``statsforecast`` [1]_ by Nixtla.
  The ``statsforecast`` implementation is a mirror of Hyndman's forecast::ets [2]_.
  
  Automatically selects the best ETS (Error, Trend, Seasonality) 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 $M$ multiplicative, $A$ additive, $Z$
  optimized or $N$ omitted components. The `model` string parameter defines the ETS
  equations: E in [$M, A, Z$], T in [$N, A, M, Z$], and S in [$N, A, M, Z$].
  
  For example when model='ANN' (additive error, no trend, and no seasonality), ETS
  will explore only a simple exponential smoothing.
  
  If the component is selected as 'Z', it operates as a placeholder to ask the
  AutoETS 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.
  damped : bool
      A parameter that 'dampens' the trend.
  
  References
  ----------
  .. [1] https://nixtla.github.io/statsforecast/models.html#autoets
  .. [2] https://github.com/robjhyndman/forecast
  
  See Also
  --------
  AutoETS
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docstr: Create underlying forecaster instance.
docstr_tok: ["Create","underlying","forecaster","instance","."]
code: def _get_statsforecast_class(self):
      """Create underlying forecaster instance."""
      from statsforecast.models import AutoETS
  
      return AutoETS
  
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 2 for calling the StatsForecastAutoETS obj's underlying member method code with example usage: obj._get_statsforecast_class() and returns: AutoETS