model_selection.TimeSeriesSplit()

Time Series cross-validator

Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. In each split, test indices must be higher than before, and thus shuffling in cross validator is inappropriate.

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

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

Read more in the User Guide.

Notes

The training set has size i * n_samples // (n_splits + 1) + n_samples % (n_splits + 1) in the i``th split, with a test set of size ``n_samples//(n_splits + 1), where n_samples is the number of samples.

Examples

Methods

__init__(n_splits=3) [source]

get_n_splits(X=None, y=None, groups=None) [source]

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. groups : object Always ignored, exists for compatibility.
Returns:	n_splits : int Returns the number of splitting iterations in the cross-validator.

split(X, y=None, groups=None) [source]

Generate indices to split data into training and test set.

Parameters:	X : array-like, shape (n_samples, n_features) Training data, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Always ignored, exists for compatibility. groups : array-like, with shape (n_samples,), optional Always ignored, exists for compatibility.
Returns:	train : ndarray The training set indices for that split. test : ndarray The testing set indices for that split.