class_docstr: Base forecaster template class.
  
  The base forecaster specifies the methods and method signatures that all forecasters
  have to implement.
  
  Specific implementations of these methods is deferred to concrete forecasters.
class_docstr_tok: ["Base","forecaster","template","class",".","The","base","forecaster","specifies","the","methods","and","method","signatures","that","all","forecasters","have","to","implement",".","Specific","implementations","of","these","methods","is","deferred","to","concrete","forecasters","."]
code: def __init__(self):
      self._is_fitted = False
  
      self._y = None
      self._X = None
  
      # forecasting horizon
      self._fh = None
      self._cutoff = None  # reference point for relative fh
  
      self._converter_store_y = (
          dict()
      )  # storage dictionary for in/output conversion
  
      super().__init__()
      _check_estimator_deps(self)
  
imports: from copy import deepcopy
  from itertools import product
  import numpy
  import pandas
  from sktime.base import BaseEstimator
  from sktime.datatypes import VectorizedDF, check_is_scitype, convert_to, get_cutoff, mtype_to_scitype, scitype_to_mtype, update_data
  from sktime.forecasting.base._fh import ForecastingHorizon
  from sktime.utils.datetime import _shift
  from sktime.utils.validation._dependencies import _check_estimator_deps
  from sktime.utils.validation.forecasting import check_alpha, check_cv, check_fh, check_X
  from sktime.utils.validation.series import check_equal_time_index
  from sktime.utils.warnings import warn
label_desc: Use image node_id 1 to create a new BaseForecaster object from inherited base classes: BaseEstimator with example: obj = BaseForecaster()