class_docstr: Custom forecaster. todo: write docstring.
  
  todo: describe your custom forecaster here
  
  Parameters
  ----------
  parama : int
      descriptive explanation of parama
  paramb : string, optional (default='default')
      descriptive explanation of paramb
  paramc : boolean, optional (default= whether paramb is not the default)
      descriptive explanation of paramc
  and so on
  est : sktime.estimator, BaseEstimator descendant
      descriptive explanation of est
  est2: another estimator
      descriptive explanation of est2
  and so on
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docstr: Forecast time series at future horizon.
  
  private _predict containing the core logic, called from predict
  
  State required:
      Requires state to be "fitted".
  
  Accesses in self:
      Fitted model attributes ending in "_"
      self.cutoff
  
  Parameters
  ----------
  fh : guaranteed to be ForecastingHorizon or None, optional (default=None)
      The forecasting horizon with the steps ahead to to predict.
      If not passed in _fit, guaranteed to be passed here
  X : sktime time series object, optional (default=None)
      guaranteed to be of an mtype in self.get_tag("X_inner_mtype")
      Exogeneous time series for the forecast
  
  Returns
  -------
  y_pred : sktime time series object
      should be of the same type as seen in _fit, as in "y_inner_mtype" tag
      Point predictions
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code: def _predict(self, fh, X):
      """Forecast time series at future horizon.
  
      private _predict containing the core logic, called from predict
  
      State required:
          Requires state to be "fitted".
  
      Accesses in self:
          Fitted model attributes ending in "_"
          self.cutoff
  
      Parameters
      ----------
      fh : guaranteed to be ForecastingHorizon or None, optional (default=None)
          The forecasting horizon with the steps ahead to to predict.
          If not passed in _fit, guaranteed to be passed here
      X : sktime time series object, optional (default=None)
          guaranteed to be of an mtype in self.get_tag("X_inner_mtype")
          Exogeneous time series for the forecast
  
      Returns
      -------
      y_pred : sktime time series object
          should be of the same type as seen in _fit, as in "y_inner_mtype" tag
          Point predictions
      """
  
file_path: sktime/extension_templates/forecasting.py
imports: from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 3 for calling the MyForecaster obj's underlying member method code with example usage: obj._predict(fh, X) without return types