class_docstr: Delegator mixin that delegates all methods to wrapped forecaster.
  
  Delegates inner forecaster methods to a wrapped estimator.
      Wrapped estimator is value of attribute with name self._delegate_name.
      By default, this is "estimator_", i.e., delegates to self.estimator_
      To override delegation, override _delegate_name attribute in child class.
  
  Delegates the following inner underscore methods:
      _fit, _predict, _update
      _predict_interval, _predict_quantiles, _predict_var, _predict_proba
  
  Does NOT delegate get_params, set_params.
      get_params, set_params will hence use one additional nesting level by default.
  
  Does NOT delegate or copy tags, this should be done in a child class if required.
class_docstr_tok: ["Delegator","mixin","that","delegates","all","methods","to","wrapped","forecaster",".","Delegates","inner","forecaster","methods","to","a","wrapped","estimator",".","Wrapped","estimator","is","value","of","attribute","with","name","self._delegate_name",".","By","default",",","this","is","``","estimator_","''",",","i.e.",",","delegates","to","self.estimator_","To","override","delegation",",","override","_delegate_name","attribute","in","child","class",".","Delegates","the","following","inner","underscore","methods",":","_fit",",","_predict",",","_update","_predict_interval",",","_predict_quantiles",",","_predict_var",",","_predict_proba","Does","NOT","delegate","get_params",",","set_params",".","get_params",",","set_params","will","hence","use","one","additional","nesting","level","by","default",".","Does","NOT","delegate","or","copy","tags",",","this","should","be","done","in","a","child","class","if","required","."]
docstr: Forecast variance at future horizon.
  
  private _predict_var containing the core logic, called from predict_var
  
  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 : pd.DataFrame, optional (default=None)
      Exogenous time series
  cov : bool, optional (default=False)
      if True, computes covariance matrix forecast.
      if False, computes marginal variance forecasts.
  
  Returns
  -------
  pred_var : pd.DataFrame, format dependent on `cov` variable
      If cov=False:
          Column names are exactly those of `y` passed in `fit`/`update`.
              For nameless formats, column index will be a RangeIndex.
          Row index is fh. Entries are variance forecasts, for var in col index.
      If cov=True:
          Column index is a multiindex: 1st level is variable names (as above)
              2nd level is fh.
          Row index is fh.
          Entries are (co-)variance forecasts, for var in col index, and
              covariance between time index in row and col.
docstr_tok: ["Forecast","variance","at","future","horizon",".","private","_predict_var","containing","the","core","logic",",","called","from","predict_var","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",":","pd.DataFrame",",","optional","(","default=None",")","Exogenous","time","series","cov",":","bool",",","optional","(","default=False",")","if","True",",","computes","covariance","matrix","forecast",".","if","False",",","computes","marginal","variance","forecasts",".","Returns","--","--","--","-","pred_var",":","pd.DataFrame",",","format","dependent","on","`","cov","`","variable","If","cov=False",":","Column","names","are","exactly","those","of","`","y","`","passed","in","`","fit","`","\/","`","update","`",".","For","nameless","formats",",","column","index","will","be","a","RangeIndex",".","Row","index","is","fh",".","Entries","are","variance","forecasts",",","for","var","in","col","index",".","If","cov=True",":","Column","index","is","a","multiindex",":","1st","level","is","variable","names","(","as","above",")","2nd","level","is","fh",".","Row","index","is","fh",".","Entries","are","(","co-",")","variance","forecasts",",","for","var","in","col","index",",","and","covariance","between","time","index","in","row","and","col","."]
code: def _predict_var(self, fh, X=None, cov=False):
      """Forecast variance at future horizon.
  
      private _predict_var containing the core logic, called from predict_var
  
      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 : pd.DataFrame, optional (default=None)
          Exogenous time series
      cov : bool, optional (default=False)
          if True, computes covariance matrix forecast.
          if False, computes marginal variance forecasts.
  
      Returns
      -------
      pred_var : pd.DataFrame, format dependent on `cov` variable
          If cov=False:
              Column names are exactly those of `y` passed in `fit`/`update`.
                  For nameless formats, column index will be a RangeIndex.
              Row index is fh. Entries are variance forecasts, for var in col index.
          If cov=True:
              Column index is a multiindex: 1st level is variable names (as above)
                  2nd level is fh.
              Row index is fh.
              Entries are (co-)variance forecasts, for var in col index, and
                  covariance between time index in row and col.
      """
      estimator = self._get_delegate()
      return estimator.predict_var(fh=fh, X=X, cov=cov)
  
file_path: sktime/sktime/forecasting/base/_delegate.py
imports: from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 8 for calling the _DelegatedForecaster obj's underlying member method code with example usage: obj._predict_var(fh, X, cov) and returns: estimator