README.md

TSCV: Time Series Cross-Validation

This repository is a scikit-learn extension for time series cross-validation. It introduces gaps between the training set and the test set, which mitigates the temporal dependence of time series and prevents information leak.

Installation

pip install tscv

Usage

This extension defines 3 cross-validator classes and 1 function:

• GapLeavePOut
• GapKFold
• GapWalkForward
• gap_train_test_split

The three classes can all be passed, as the cv argument, to the cross_val_score function in scikit-learn, just like the native cross-validator classes in scikit-learn.

The one function is an alternative to the train_test_split function in scikit-learn.

Examples

The following example uses GapKFold instead of KFold as the cross-validator.

import numpy as np
from sklearn import datasets
from sklearn import svm
from sklearn.model_selection import cross_val_score
from tscv import GapKFold

iris = datasets.load_iris()
clf = svm.SVC(kernel='linear', C=1)

# use GapKFold as the cross-validator
cv = GapKFold(n_splits=5, gap_before=5, gap_after=5)
scores = cross_val_score(clf, iris.data, iris.target, cv=cv)

The following example uses gap_train_test_split to split the data set into the training set and the test set.

import numpy as np
from tscv import gap_train_test_split

X, y = np.arange(20).reshape((10, 2)), np.arange(10)
X_train, X_test, y_train, y_test = gap_train_test_split(X, y, test_size=2, gap_size=2)

Support

See the documentation here.

If you need any further help, please use the issue tracker.

Authors

This extension is mainly developed by me, Wenjie Zheng.

The GapWalkForward cross-validator is adapted from the TimeSeriesSplit of scikit-learn.

Acknowledgment

• I would like to thank Christoph Bergmeir, Prabir Burman, and Jeffrey Racine for the helpful discussion.
• I would like to thank Jacques Joubert for encouraging me to develop this package.

License

BSD-3-Clause