Skip to main content

Time series cross-validation

Project description

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.

train gap test


This repository is not registered, but you can clone it into your own project and use it with ease.

git clone tscv
mkdir YOURPROJECT/tscv
cp tscv/ YOURPROJECT/tscv/
cp tscv/ YOURPROJECT/tscv/__init__

YOURPROJECT is the name of your project folder.


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

  • GapLeaevPOut
  • 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.


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.



Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tscv-0.0.1.tar.gz (7.5 kB view hashes)

Uploaded source

Built Distribution

tscv-0.0.1-py3-none-any.whl (8.9 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page