class_docstr: Base class for all forecasters in skforecast. All forecasters should specify
  all the parameters that can be set at the class level in their ``__init__``.     
docstr: Create training matrices from univariate time series and exogenous
  variables.
  
  Parameters
  ----------
  y : pandas Series
      Training time series.
  exog : pandas Series, pandas DataFrame, default `None`
      Exogenous variable/s included as predictor/s. Must have the same
      number of observations as `y` and their indexes must be aligned.
  
  Returns
  -------
  X_train : pandas DataFrame
      Training values (predictors).
      Shape: (len(y) - self.max_lag, len(self.lags))
  y_train : pandas Series
      Values (target) of the time series related to each row of `X_train`.
      Shape: (len(y) - self.max_lag, )
code: def create_train_X_y(
      self,
      y: pd.Series,
      exog: Optional[Union[pd.Series, pd.DataFrame]] = None,
  ) -> Tuple[pd.DataFrame, pd.Series]:
      """
      Create training matrices from univariate time series and exogenous
      variables.
  
      Parameters
      ----------
      y : pandas Series
          Training time series.
      exog : pandas Series, pandas DataFrame, default `None`
          Exogenous variable/s included as predictor/s. Must have the same
          number of observations as `y` and their indexes must be aligned.
  
      Returns
      -------
      X_train : pandas DataFrame
          Training values (predictors).
          Shape: (len(y) - self.max_lag, len(self.lags))
      y_train : pandas Series
          Values (target) of the time series related to each row of `X_train`.
          Shape: (len(y) - self.max_lag, )
  
      """
  
      pass
  
file_path: skforecast/skforecast/ForecasterBase/ForecasterBase.py
imports: from abc import ABC, abstractmethod
  from typing import Union, Dict, List, Tuple, Any, Optional
  import logging
  import pandas
label_desc: Use image node_id 1 for calling the ForecasterBase obj's underlying member method code with example usage: obj.create_train_X_y(y, exog) without return types