docstr: Auto-CES based on `Statsforecasts package
  <https://github.com/Nixtla/statsforecast>`_.
  
  Automatically selects the best Complex Exponential Smoothing model using an information criterion.
  <https://onlinelibrary.wiley.com/doi/full/10.1002/nav.22074>
  
  We refer to the `statsforecast AutoCES documentation
  <https://nixtla.github.io/statsforecast/src/core/models.html#autoces>`_
  for the exhaustive documentation of the arguments.
  
  Parameters
  ----------
  autoces_args
      Positional arguments for ``statsforecasts.models.AutoCES``.
  autoces_kwargs
      Keyword arguments for ``statsforecasts.models.AutoCES``.
  
  Examples
  --------
  >>> from darts.datasets import AirPassengersDataset
  >>> from darts.models import StatsForecastAutoCES
  >>> series = AirPassengersDataset().load()
  >>> # define StatsForecastAutoCES parameters
  >>> model = StatsForecastAutoCES(season_length=12, model="Z")
  >>> model.fit(series)
  >>> pred = model.predict(6)
  >>> pred.values()
  array([[453.03417969],
         [429.34039307],
         [488.64471436],
         [500.28955078],
         [519.79962158],
         [586.47503662]])
code: def __init__(self, *autoces_args, **autoces_kwargs):
      """Auto-CES based on `Statsforecasts package
      <https://github.com/Nixtla/statsforecast>`_.
  
      Automatically selects the best Complex Exponential Smoothing model using an information criterion.
      <https://onlinelibrary.wiley.com/doi/full/10.1002/nav.22074>
  
      We refer to the `statsforecast AutoCES documentation
      <https://nixtla.github.io/statsforecast/src/core/models.html#autoces>`_
      for the exhaustive documentation of the arguments.
  
      Parameters
      ----------
      autoces_args
          Positional arguments for ``statsforecasts.models.AutoCES``.
      autoces_kwargs
          Keyword arguments for ``statsforecasts.models.AutoCES``.
  
      Examples
      --------
      >>> from darts.datasets import AirPassengersDataset
      >>> from darts.models import StatsForecastAutoCES
      >>> series = AirPassengersDataset().load()
      >>> # define StatsForecastAutoCES parameters
      >>> model = StatsForecastAutoCES(season_length=12, model="Z")
      >>> model.fit(series)
      >>> pred = model.predict(6)
      >>> pred.values()
      array([[453.03417969],
             [429.34039307],
             [488.64471436],
             [500.28955078],
             [519.79962158],
             [586.47503662]])
      """
      super().__init__()
      self.model = SFAutoCES(*autoces_args, **autoces_kwargs)
  
file_path: darts/darts/models/forecasting/sf_auto_ces.py
imports: from statsforecast.models import AutoCES
  from darts import TimeSeries
  from darts.models.forecasting.forecasting_model import LocalForecastingModel
label_desc: Use image node_id 1 to create a new StatsForecastAutoCES object from inherited base classes: LocalForecastingModel with example: obj = StatsForecastAutoCES()