Skip to content
Time Series Cross-Validation -- an extension for scikit-learn
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
tscv `Gapold` --> `GapKFold` in `__all__` May 19, 2019
.gitignore Initial commit May 14, 2019
LICENSE update license May 14, 2019 add ways of contributing May 16, 2019
requirements.txt packaging May 14, 2019 Update May 16, 2019
train-gap-test.svg new train-gap-test.svg May 15, 2019

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.


pip install tscv


pip install tscv --upgrade

I recommend you to update it often.


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.


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,,, 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)


See the documentation here.

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


  • Report bugs in the issue tracker
  • Express your use cases in the issue tracker


This extension is mainly developed by me, Wenjie Zheng.

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


  • 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.



You can’t perform that action at this time.