docstr: Auto-Theta based on `Statsforecasts package
  <https://github.com/Nixtla/statsforecast>`_.
  
  Automatically selects the best Theta (Standard Theta Model (‘STM’), Optimized Theta Model (‘OTM’),
  Dynamic Standard Theta Model (‘DSTM’), Dynamic Optimized Theta Model (‘DOTM’)) model using mse.
  <https://www.sciencedirect.com/science/article/pii/S0169207016300243>
  
  It is probabilistic, whereas :class:`FourTheta` is not.
  
  We refer to the `statsforecast AutoTheta documentation
  <https://nixtla.github.io/statsforecast/src/core/models.html#autotheta>`_
  for the exhaustive documentation of the arguments.
  
  Parameters
  ----------
  autotheta_args
      Positional arguments for ``statsforecasts.models.AutoTheta``.
  autotheta_kwargs
      Keyword arguments for ``statsforecasts.models.AutoTheta``.
  
  Examples
  --------
  >>> from darts.datasets import AirPassengersDataset
  >>> from darts.models import StatsForecastAutoTheta
  >>> series = AirPassengersDataset().load()
  >>> # define StatsForecastAutoTheta parameters
  >>> model = StatsForecastAutoTheta(season_length=12)
  >>> model.fit(series)
  >>> pred = model.predict(6)
  >>> pred.values()
  array([[442.94078295],
         [432.22936898],
         [495.30609727],
         [482.30625563],
         [487.49312172],
         [555.57902659]])
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code: def __init__(self, *autotheta_args, **autotheta_kwargs):
      """Auto-Theta based on `Statsforecasts package
      <https://github.com/Nixtla/statsforecast>`_.
  
      Automatically selects the best Theta (Standard Theta Model (‘STM’), Optimized Theta Model (‘OTM’),
      Dynamic Standard Theta Model (‘DSTM’), Dynamic Optimized Theta Model (‘DOTM’)) model using mse.
      <https://www.sciencedirect.com/science/article/pii/S0169207016300243>
  
      It is probabilistic, whereas :class:`FourTheta` is not.
  
      We refer to the `statsforecast AutoTheta documentation
      <https://nixtla.github.io/statsforecast/src/core/models.html#autotheta>`_
      for the exhaustive documentation of the arguments.
  
      Parameters
      ----------
      autotheta_args
          Positional arguments for ``statsforecasts.models.AutoTheta``.
      autotheta_kwargs
          Keyword arguments for ``statsforecasts.models.AutoTheta``.
  
      Examples
      --------
      >>> from darts.datasets import AirPassengersDataset
      >>> from darts.models import StatsForecastAutoTheta
      >>> series = AirPassengersDataset().load()
      >>> # define StatsForecastAutoTheta parameters
      >>> model = StatsForecastAutoTheta(season_length=12)
      >>> model.fit(series)
      >>> pred = model.predict(6)
      >>> pred.values()
      array([[442.94078295],
             [432.22936898],
             [495.30609727],
             [482.30625563],
             [487.49312172],
             [555.57902659]])
      """
      super().__init__()
      self.model = SFAutoTheta(*autotheta_args, **autotheta_kwargs)
  
file_path: darts/darts/models/forecasting/sf_auto_theta.py
imports: from statsforecast.models import AutoTheta
  from darts import TimeSeries
  from darts.models.components.statsforecast_utils import create_normal_samples, one_sigma_rule, unpack_sf_dict
  from darts.models.forecasting.forecasting_model import LocalForecastingModel
label_desc: Use image node_id 1 to create a new StatsForecastAutoTheta object from inherited base classes: LocalForecastingModel with example: obj = StatsForecastAutoTheta()