class_docstr: Base class for time series cross validators
  
  Based on the scikit-learn base cross-validator with alterations to fit the
  time series interface.
class_docstr_tok: ["Base","class","for","time","series","cross","validators","Based","on","the","scikit-learn","base","cross-validator","with","alterations","to","fit","the","time","series","interface","."]
docstr: Generate indices to split data into training and test sets
  
  Parameters
  ----------
  y : array-like or iterable, shape=(n_samples,)
      The time-series array.
  
  X : array-like, shape=[n_obs, n_vars], optional (default=None)
      An optional 2-d array of exogenous variables.
  
  Yields
  ------
  train : np.ndarray
      The training set indices for the split
  
  test : np.ndarray
      The test set indices for the split
docstr_tok: ["Generate","indices","to","split","data","into","training","and","test","sets","Parameters","--","--","--","--","--","y",":","array-like","or","iterable",",","shape=","(","n_samples",",",")","The","time-series","array",".","X",":","array-like",",","shape=","[","n_obs",",","n_vars","]",",","optional","(","default=None",")","An","optional","2-d","array","of","exogenous","variables",".","Yields","--","--","--","train",":","np.ndarray","The","training","set","indices","for","the","split","test",":","np.ndarray","The","test","set","indices","for","the","split"]
code: def split(self, y, X=None):
      """Generate indices to split data into training and test sets
  
      Parameters
      ----------
      y : array-like or iterable, shape=(n_samples,)
          The time-series array.
  
      X : array-like, shape=[n_obs, n_vars], optional (default=None)
          An optional 2-d array of exogenous variables.
  
      Yields
      ------
      train : np.ndarray
          The training set indices for the split
  
      test : np.ndarray
          The test set indices for the split
      """
      y, X = indexable(y, X)
      indices = np.arange(y.shape[0])
      for train_index, test_index in self._iter_train_test_masks(y, X):
          train_index = indices[train_index]
          test_index = indices[test_index]
          yield train_index, test_index
  
imports: import abc
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import indexable
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 3 for calling the BaseTSCrossValidator obj's underlying member method code with example usage: obj.split(y, X) without return types