sklearn.model_selection.TimeSeriesSplit

class sklearn.model_selection.TimeSeriesSplit(n_splits='warn', max_train_size=None)

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.

Parameters:

n_splits : int, default=3

Number of splits. Must be at least 2.

Changed in version 0.20: n_splits default value will change from 3 to 5 in v0.22.

max_train_size : int, optional

Maximum size for a single training set.

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

>>> 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(n_splits=5)
>>> print(tscv)  # doctest: +NORMALIZE_WHITESPACE
TimeSeriesSplit(max_train_size=None, n_splits=5)
>>> for train_index, test_index in tscv.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: [0 1 2] TEST: [3]
TRAIN: [0 1 2 3] TEST: [4]
TRAIN: [0 1 2 3 4] TEST: [5]

Methods

get_n_splits([X, y, groups])	Returns the number of splitting iterations in the cross-validator
split(X[, y, groups])	Generate indices to split data into training and test set.

__init__(n_splits='warn', max_train_size=None)

Initialize self. See help(type(self)) for accurate signature.

get_n_splits(X=None, y=None, groups=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.

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)

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,)

Always ignored, exists for compatibility.

Yields:

train : ndarray

The training set indices for that split.

test : ndarray

The testing set indices for that split.

Examples using sklearn.model_selection.TimeSeriesSplit

Visualizing cross-validation behavior in scikit-learn