class_docstr: Shared methods across time-series forecasting transformers.
  
  Parameters
  ----------
  variables: str, int, or list of strings or integers, default=None.
      The variables to use to create the new features.
  
  {missing_values}
  
  {drop_original}
  
  Attributes
  ----------
  {feature_names_in_}
  
  {n_features_in_}
class_docstr_tok: ["Shared","methods","across","time-series","forecasting","transformers",".","Parameters","--","--","--","--","--","variables",":","str",",","int",",","or","list","of","strings","or","integers",",","default=None",".","The","variables","to","use","to","create","the","new","features",".","{","missing_values","}","{","drop_original","}","Attributes","--","--","--","--","--","{","feature_names_in_","}","{","n_features_in_","}"]
code: def __init__(
      self,
      variables: Union[None, int, str, List[Union[str, int]]] = None,
      missing_values: str = "raise",
      drop_original: bool = False,
  ) -> None:
      if missing_values not in ["raise", "ignore"]:
          raise ValueError(
              "missing_values takes only values 'raise' or 'ignore'. "
              f"Got {missing_values} instead."
          )
  
      if not isinstance(drop_original, bool):
          raise ValueError(
              "drop_original takes only boolean values True and False. "
              f"Got {drop_original} instead."
          )
  
      self.variables = _check_init_parameter_variables(variables)
      self.missing_values = missing_values
      self.drop_original = drop_original
  
imports: from typing import List, Optional, Union
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils.validation import check_is_fitted
  from feature_engine._base_transformers.mixins import GetFeatureNamesOutMixin
  from feature_engine._docstrings.fit_attributes import _feature_names_in_docstring, _n_features_in_docstring
  from feature_engine._docstrings.init_parameters import _drop_original_docstring, _missing_values_docstring
  from feature_engine._docstrings.methods import _fit_not_learn_docstring
  from feature_engine._docstrings.substitute import Substitution
  from feature_engine._variable_handling.init_parameter_checks import _check_init_parameter_variables
  from feature_engine._variable_handling.variable_type_selection import _find_or_check_numerical_variables
  from feature_engine.dataframe_checks import _check_contains_inf, _check_contains_na, _check_X_matches_training_df, check_X
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 1 to create a new BaseForecastTransformer object from inherited base classes: BaseEstimator, TransformerMixin, GetFeatureNamesOutMixin with example: obj = BaseForecastTransformer(variables, missing_values, drop_original)