class_docstr: Base class for interfacing statsmodels forecasting algorithms.
class_docstr_tok: ["Base","class","for","interfacing","statsmodels","forecasting","algorithms","."]
docstr: Construct confidence interval at specified `alpha` for each timestep.
  
  Parameters
  ----------
  prediction_results : PredictionResults
      results class, as returned by ``self._fitted_forecaster.get_prediction``
  alpha : float
      one minus nominal coverage
  
  Returns
  -------
  pd.DataFrame
      confidence intervals at each timestep
  
      The dataframe must have at least two columns ``lower`` and ``upper``, and
      the row indices must be integers relative to ``self.cutoff``. Order of
      columns do not matter, and row indices must be a superset of relative
      integer horizon of ``fh``.
docstr_tok: ["Construct","confidence","interval","at","specified","`","alpha","`","for","each","timestep",".","Parameters","--","--","--","--","--","prediction_results",":","PredictionResults","results","class",",","as","returned","by","``","self._fitted_forecaster.get_prediction","``","alpha",":","float","one","minus","nominal","coverage","Returns","--","--","--","-","pd.DataFrame","confidence","intervals","at","each","timestep","The","dataframe","must","have","at","least","two","columns","``","lower","``","and","``","upper","``",",","and","the","row","indices","must","be","integers","relative","to","``","self.cutoff","``",".","Order","of","columns","do","not","matter",",","and","row","indices","must","be","a","superset","of","relative","integer","horizon","of","``","fh","``","."]
code: def _extract_conf_int(prediction_results, alpha) -> pd.DataFrame:
      """Construct confidence interval at specified `alpha` for each timestep.
  
      Parameters
      ----------
      prediction_results : PredictionResults
          results class, as returned by ``self._fitted_forecaster.get_prediction``
      alpha : float
          one minus nominal coverage
  
      Returns
      -------
      pd.DataFrame
          confidence intervals at each timestep
  
          The dataframe must have at least two columns ``lower`` and ``upper``, and
          the row indices must be integers relative to ``self.cutoff``. Order of
          columns do not matter, and row indices must be a superset of relative
          integer horizon of ``fh``.
      """
      del (
          prediction_results,
          alpha,
      )  # tools like ``vulture`` may complain as unused
  
      raise NotImplementedError("abstract method")
  
file_path: sktime/sktime/forecasting/base/adapters/_statsmodels.py
imports: import inspect
  import numpy
  import pandas
  from sktime.forecasting.base import BaseForecaster
  from sktime.utils.warnings import warn
label_desc: Use image node_id 6 for calling the _StatsModelsAdapter obj's underlying member method code with example usage: obj._extract_conf_int(prediction_results, alpha) without return types