scikit-learn
diff --git a/‎sklearn/model_selection/__init__.py
Lines changed: 2 additions & 0 deletions b/‎sklearn/model_selection/__init__.py
Lines changed: 2 additions & 0 deletions
diff --git a/‎sklearn/model_selection/_split.py
Lines changed: 86 additions & 0 deletions b/‎sklearn/model_selection/_split.py
Lines changed: 86 additions & 0 deletions
@@ -2,6 +2,7 @@
 from ._split import KFold
 from ._split import LabelKFold
 from ._split import StratifiedKFold
+from ._split import HomogeneousTimeSeriesCV
 from ._split import LeaveOneLabelOut
 from ._split import LeaveOneOut
 from ._split import LeavePLabelOut
@@ -27,6 +28,7 @@
 
 __all__ = ('BaseCrossValidator',
            'GridSearchCV',
+           'HomogeneousTimeSeriesCV',
            'KFold',
            'LabelKFold',
            'LabelShuffleSplit',
 
@@ -637,6 +637,92 @@ def split(self, X, y, labels=None):
         """
         return super(StratifiedKFold, self).split(X, y, labels)
 
+class HomogeneousTimeSeriesCV(_BaseKFold):
+    """Homogeneous Time Series cross-validator
+
+    Provides train/test indices to split time series data in train/test sets.
+
+    This cross-validation object is a variation of KFold.
+    In iteration k, it returns first k folds as train sets and k+1 fold as
+    test sets.
+
+    Read more in the :ref:`User Guide <cross_validation>`.
+
+    Parameters
+    ----------
+    n_folds : int, default=3
+        Number of folds. Must be at least 2.
+
+    Examples
+    --------
+    >>> from sklearn.model_selection import HomogeneousTimeSeriesCV
+    >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
+    >>> y = np.array([1, 2, 3, 4])
+    >>> htscv = HomogeneousTimeSeriesCV(n_folds=4)
+    >>> htscv.get_n_splits(X)
+    3
+    >>> print(htscv)  # doctest: +NORMALIZE_WHITESPACE
+    KFold(n_folds=2, random_state=None, shuffle=False)
+    >>> for train_index, test_index in htscv.split(X):
+    ...    print("TRAIN:", train_index, "TEST:", test_index)
+    ...    X_train, X_test = X[train_index], X[test_index]
+    ...    y_train, y_test = y[train_index], y[test_index]
+    TRAIN: [0] TEST: [1]
+    TRAIN: [0 1] TEST: [2]
+    TRAIN: [1 2 3] TEST: [3]
+
+    Notes
+    -----
+    The first ``n_samples % n_folds`` folds have size
+    ``n_samples // n_folds + 1``, other folds have size
+    ``n_samples // n_folds``, where ``n_samples`` is the number of samples.
+
+    Number of splitting iterations in this cross-validator, n_folds-1,
+    is not equal to other KFold based cross-validators'.
+
+    See also
+    --------
+    """
+    def __init__(self, n_folds=3):
+        super(HomogeneousTimeSeriesCV, self).__init__(n_folds,
+                                                      shuffle=False,
+                                                      random_state=None)
+
+    def split(self, X, y=None, labels=None):
+        n_samples = _num_samples(X)
+        n_folds = self.n_folds
+        indices = np.arange(n_samples)
+        fold_sizes = (n_samples // n_folds) * np.ones(n_folds, dtype=np.int)
+        fold_sizes[:n_samples % n_folds] += 1
+        current = 0
+        for fold_size in fold_sizes:
+            start, stop = current, current + fold_size
+            if current != 0:
+                yield indices[:start], indices[start:stop]
+            current = stop
+
+    def get_n_splits(self, X=None, y=None, labels=None):
+        """Returns the number of splitting iterations in the cross-validator
+
+        Parameters
+        ----------
+        X : object
+            Always ignored, exists for compatibility.
+
+        y : object
+            Always ignored, exists for compatibility.
+
+        labels : object
+            Always ignored, exists for compatibility.
+
+        Returns
+        -------
+        n_splits : int
+            Returns the number of splitting iterations in the cross-validator.
+        """
+        return self.n_folds-1
+
+
 class LeaveOneLabelOut(BaseCrossValidator):
     """Leave One Label Out cross-validator