class_docstr: Base class for interfacing statsmodels forecasting algorithms.
class_docstr_tok: ["Base","class","for","interfacing","statsmodels","forecasting","algorithms","."]
docstr: Update used internally in update.
docstr_tok: ["Update","used","internally","in","update","."]
code: def _update(self, y, X=None, update_params=True):
      """Update used internally in update."""
      if update_params or self.is_composite():
          super()._update(y, X, update_params=update_params)
      else:
          if not hasattr(self._fitted_forecaster, "append"):
              warn(
                  f"NotImplementedWarning: {self.__class__.__name__} "
                  f"can not accept new data when update_params=False. "
                  f"Call with update_params=True to refit with new data.",
                  obj=self,
              )
          else:
              # only append unseen data to fitted forecaster
              index_diff = y.index.difference(
                  self._fitted_forecaster.fittedvalues.index
              )
              if index_diff.isin(y.index).all():
                  y = y.loc[index_diff]
              self._fitted_forecaster = self._fitted_forecaster.append(
                  y
              )
  
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 4 for calling the _StatsModelsAdapter obj's underlying member method code with example usage: obj._update(y, X, update_params) without return types