class_docstr: Base class for interfacing tbats forecasting algorithms.
class_docstr_tok: ["Base","class","for","interfacing","tbats","forecasting","algorithms","."]
docstr: Update time series to incremental training data.
  
  Derived from example provided by core devs in TBATS repository
  https://github.com/intive-DataScience/tbats/blob/master/examples/
  
  Parameters
  ----------
  y : pd.Series
      Target time series to which to fit the forecaster.
  X : pd.DataFrame, optional (default=None)
      Exogenous variables (ignored)
  update_params : bool, optional (default=True)
      whether model parameters should be updated
  
  Returns
  -------
  self : reference to self
docstr_tok: ["Update","time","series","to","incremental","training","data",".","Derived","from","example","provided","by","core","devs","in","TBATS","repository","https",":","\/\/github.com\/intive-DataScience\/tbats\/blob\/master\/examples\/","Parameters","--","--","--","--","--","y",":","pd.Series","Target","time","series","to","which","to","fit","the","forecaster",".","X",":","pd.DataFrame",",","optional","(","default=None",")","Exogenous","variables","(","ignored",")","update_params",":","bool",",","optional","(","default=True",")","whether","model","parameters","should","be","updated","Returns","--","--","--","-","self",":","reference","to","self"]
code: def _update(self, y, X=None, update_params=True):
      """Update time series to incremental training data.
  
      Derived from example provided by core devs in TBATS repository
      https://github.com/intive-DataScience/tbats/blob/master/examples/
  
      Parameters
      ----------
      y : pd.Series
          Target time series to which to fit the forecaster.
      X : pd.DataFrame, optional (default=None)
          Exogenous variables (ignored)
      update_params : bool, optional (default=True)
          whether model parameters should be updated
  
      Returns
      -------
      self : reference to self
      """
      if update_params:
          # update model state and refit parameters
          # _fit re-runs model instantiation which triggers refit
          self._fit(y=self._y, X=None, fh=self._fh)
  
      else:
          # update model state without refitting parameters
          # out-of-box fit tbats method will not refit parameters
          self._forecaster.fit(y=self._y)
  
      return self
  
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 5 for calling the _TbatsAdapter obj's underlying member method code with example usage: obj._update(y, X, update_params) and returns: self