class_docstr: Base class for interfacing tbats forecasting algorithms.
class_docstr_tok: ["Base","class","for","interfacing","tbats","forecasting","algorithms","."]
docstr: Combine in- & out-sample prediction, slices given fh.
  
  Parameters
  ----------
  y_in_sample : pd.Series
      In-sample prediction
  y_out_sample : pd.Series
      Out-sample prediction
  
  Returns
  -------
  pd.Series
      y_pred, sliced by fh
docstr_tok: ["Combine","in-","&","out-sample","prediction",",","slices","given","fh",".","Parameters","--","--","--","--","--","y_in_sample",":","pd.Series","In-sample","prediction","y_out_sample",":","pd.Series","Out-sample","prediction","Returns","--","--","--","-","pd.Series","y_pred",",","sliced","by","fh"]
code: def _get_y_pred(self, y_in_sample, y_out_sample):
      """Combine in- & out-sample prediction, slices given fh.
  
      Parameters
      ----------
      y_in_sample : pd.Series
          In-sample prediction
      y_out_sample : pd.Series
          Out-sample prediction
  
      Returns
      -------
      pd.Series
          y_pred, sliced by fh
      """
      y_pred = pd.concat(
          [y_in_sample, y_out_sample], ignore_index=True
      ).rename("y_pred")
      y_pred = pd.DataFrame(y_pred)
      # Workaround for slicing with negative index
      y_pred["idx"] = [
          x for x in range(-len(y_in_sample), len(y_out_sample))
      ]
      y_pred = y_pred.loc[
          y_pred["idx"].isin(self.fh.to_indexer(self.cutoff).values)
      ]
      y_pred.index = self.fh.to_absolute_index(self.cutoff)
      y_pred = y_pred["y_pred"].rename(None)
      return y_pred
  
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 7 for calling the _TbatsAdapter obj's underlying member method code with example usage: obj._get_y_pred(y_in_sample, y_out_sample) and returns: y_pred