Class TimeSeriesSplit

sklearn/model_selection/_split.py:1116–1327

Time Series cross-validator. Provides train/test indices to split time-ordered data, where other cross-validation methods are inappropriate, as they would lead to training on future data and evaluating on past data. To ensure comparable metrics across folds, samples must be equally

class TimeSeriesSplit(_BaseKFold):
    """Time Series cross-validator.

    Provides train/test indices to split time-ordered data, where other
    cross-validation methods are inappropriate, as they would lead to training
    on future data and evaluating on past data.
    To ensure comparable metrics across folds, samples must be equally spaced.
    Once this condition is met, each test set covers the same time duration,
    while the train set size accumulates data from previous splits.

    This cross-validation object is a variation of :class:`KFold`.
    In the k-th split, it returns the first k folds as the train set and the
    (k+1)-th fold as the test set.

    Note that, unlike standard cross-validation methods, successive
    training sets are supersets of those that come before them.

    Read more in the :ref:`User Guide <time_series_split>`.

    For visualisation of cross-validation behaviour and
    comparison between common scikit-learn split methods
    refer to :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py`

    .. versionadded:: 0.18

    Parameters
    ----------
    n_splits : int, default=5
        Number of splits. Must be at least 2.

        .. versionchanged:: 0.22
            ``n_splits`` default value changed from 3 to 5.

    max_train_size : int, default=None
        Maximum size for a single training set.

    test_size : int, default=None
        Used to limit the size of the test set. Defaults to
        ``n_samples // (n_splits + 1)``, which is the maximum allowed value
        with ``gap=0``.

        .. versionadded:: 0.24

    gap : int, default=0
        Number of samples to exclude from the end of each train set before
        the test set.

        .. versionadded:: 0.24

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.model_selection import TimeSeriesSplit
    >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
    >>> y = np.array([1, 2, 3, 4, 5, 6])
    >>> tscv = TimeSeriesSplit()
    >>> print(tscv)
    TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)