class_docstr: Base class for interfacing tbats forecasting algorithms.
class_docstr_tok: ["Base","class","for","interfacing","tbats","forecasting","algorithms","."]
docstr: TBATS prediction interval forecast adapter function.
  
  Parameters
  ----------
  fh : int, list, np.array or ForecastingHorizon
      Forecasting horizon
  conf_lev : float
      confidence_level for TBATS
  
  Returns
  -------
  y_pred : pd.Series
      Prediction
  y_pred_int : pd.DataFrame
      Prediction intervals
docstr_tok: ["TBATS","prediction","interval","forecast","adapter","function",".","Parameters","--","--","--","--","--","fh",":","int",",","list",",","np.array","or","ForecastingHorizon","Forecasting","horizon","conf_lev",":","float","confidence_level","for","TBATS","Returns","--","--","--","-","y_pred",":","pd.Series","Prediction","y_pred_int",":","pd.DataFrame","Prediction","intervals"]
code: def _tbats_forecast_interval(self, fh, conf_lev):
      """TBATS prediction interval forecast adapter function.
  
      Parameters
      ----------
      fh : int, list, np.array or ForecastingHorizon
          Forecasting horizon
      conf_lev : float
          confidence_level for TBATS
  
      Returns
      -------
      y_pred : pd.Series
          Prediction
      y_pred_int : pd.DataFrame
          Prediction intervals
      """
      fh = fh.to_relative(cutoff=self.cutoff)
      fh_out = fh.to_out_of_sample(cutoff=self.cutoff)
  
      if not fh.is_all_in_sample(cutoff=self.cutoff):
          steps = fh_out.to_pandas()[-1]
          _, tbats_ci = self._forecaster.forecast(
              steps=steps, confidence_level=conf_lev
          )
          out = pd.DataFrame(tbats_ci)
          # pred_int
          lower = pd.Series(out["lower_bound"])
          upper = pd.Series(out["upper_bound"])
          pred_int_oos = pd.DataFrame({"lower": lower, "upper": upper})
          pred_int_oos = pred_int_oos.iloc[fh_out.to_indexer()]
          pred_int_oos.index = fh_out.to_absolute_index(self.cutoff)
      else:
          pred_int_oos = pd.DataFrame(columns=["lower", "upper"])
  
      full_ix = fh.to_absolute_index(self.cutoff)
      pred_int = pred_int_oos.reindex(full_ix)
  
      return pred_int
  
file_path: sktime/sktime/forecasting/base/adapters/_tbats.py
imports: import numpy
  import pandas
  from sktime.forecasting.base import BaseForecaster
  from sktime.utils.validation import check_n_jobs
  from sktime.utils.validation.forecasting import check_sp
label_desc: Use image node_id 9 for calling the _TbatsAdapter obj's underlying member method code with example usage: obj._tbats_forecast_interval(fh, conf_lev) and returns: pred_int