class_docstr: Base class for interfacing tbats forecasting algorithms.
class_docstr_tok: ["Base","class","for","interfacing","tbats","forecasting","algorithms","."]
docstr: TBATS point forecast adapter function.
  
  Parameters
  ----------
  fh : int, list, np.array or ForecastingHorizon
      Forecasting horizon
  
  Returns
  -------
  y_pred : pd.Series
      Prediction
docstr_tok: ["TBATS","point","forecast","adapter","function",".","Parameters","--","--","--","--","--","fh",":","int",",","list",",","np.array","or","ForecastingHorizon","Forecasting","horizon","Returns","--","--","--","-","y_pred",":","pd.Series","Prediction"]
code: def _tbats_forecast(self, fh):
      """TBATS point forecast adapter function.
  
      Parameters
      ----------
      fh : int, list, np.array or ForecastingHorizon
          Forecasting horizon
  
      Returns
      -------
      y_pred : pd.Series
          Prediction
      """
      fh = fh.to_relative(cutoff=self.cutoff)
  
      if not fh.is_all_in_sample(cutoff=self.cutoff):
          fh_out = fh.to_out_of_sample(cutoff=self.cutoff)
          steps = fh_out.to_pandas()[-1]
          y_out = self._forecaster.forecast(
              steps=steps, confidence_level=None
          )
      else:
          y_out = nans(len(fh))
  
      # y_pred combine in and out samples
      y_in_sample = pd.Series(self._forecaster.y_hat)
      y_out_sample = pd.Series(y_out)
      y_pred = self._get_y_pred(
          y_in_sample=y_in_sample, y_out_sample=y_out_sample
      )
      y_pred.name = self._yname
  
      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 8 for calling the _TbatsAdapter obj's underlying member method code with example usage: obj._tbats_forecast(fh) and returns: y_pred