class_docstr: StatsForecast Back Adapter.
  
  StatsForecastBackAdapter is a wrapper for sktime forecasters to be used in
  StatsForecast composite models.
  
  Parameters
  ----------
  estimator : sktime forecaster
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.statsforecast import StatsForecastMSTL
  >>> from sktime.forecasting.ets import AutoETS
  
  >>> y = load_airline()
  >>> trend_forecaster = AutoETS() # doctest: +SKIP
  >>> model = StatsForecastMSTL( # doctest: +SKIP
          season_length=[3,12],
          trend_forecaster=trend_forecaster
      )
  >>> fitted_model = model.fit(y=y) # doctest: +SKIP
  >>> y_pred = fitted_model.predict(fh=[1,2,3]) # doctest: +SKIP
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docstr: Make forecasts.
  
  Parameters
  ----------
  h : int
      Forecast horizon.
  X : typing.Optional[numpy.ndarray], default=None
      Optional exogenous of shape (h, n_x).
  level : typing.Optional[typing.Tuple[int]], default=None
      Confidence levels (0-100) for prediction intervals.
  
  Returns
  -------
  y_pred : dict
      Dictionary with entries mean for point predictions and level_* for
      probabilistic predictions.
docstr_tok: ["Make","forecasts",".","Parameters","--","--","--","--","--","h",":","int","Forecast","horizon",".","X",":","typing.Optional","[","numpy.ndarray","]",",","default=None","Optional","exogenous","of","shape","(","h",",","n_x",")",".","level",":","typing.Optional","[","typing.Tuple","[","int","]","]",",","default=None","Confidence","levels","(","0-100",")","for","prediction","intervals",".","Returns","--","--","--","-","y_pred",":","dict","Dictionary","with","entries","mean","for","point","predictions","and","level_","*","for","probabilistic","predictions","."]
code: def predict(self, h, X=None, level=None):
      """Make forecasts.
  
      Parameters
      ----------
      h : int
          Forecast horizon.
      X : typing.Optional[numpy.ndarray], default=None
          Optional exogenous of shape (h, n_x).
      level : typing.Optional[typing.Tuple[int]], default=None
          Confidence levels (0-100) for prediction intervals.
  
      Returns
      -------
      y_pred : dict
          Dictionary with entries mean for point predictions and level_* for
          probabilistic predictions.
      """
      mean = self.estimator.predict(fh=range(1, h + 1), X=X)[:, 0]
      if level is None:
          return {"mean": mean}
      # if a level is passed, and if prediction_intervals has not been instantiated
      # yet
      elif self.prediction_intervals is None:
          from statsforecast.utils import ConformalIntervals
  
          self.prediction_intervals = ConformalIntervals(h=h)
  
      level = sorted(level)
      coverage = [round(_l / 100, 2) for _l in level]
  
      pred_int = self.estimator.predict_interval(
          fh=range(1, h + 1), X=X, coverage=coverage
      )
  
      return self.format_pred_int(
          "mean", mean, pred_int, coverage, level
      )
  
imports: from inspect import signature
  from typing import Dict
  from warnings import warn
  import pandas
  from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 5 for calling the StatsForecastBackAdapter obj's underlying member method code with example usage: obj.predict(h, X, level) and returns: self, dict