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 leakage.


pip install tscv


pip install tscv --upgrade


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)


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


This extension is developed mainly by Wenjie Zheng.

The GapWalkForward cross-validator is adapted from the TimeSeriesSplit of scikit-learn (see Kyle Kosic's PR scikit-learn/scikit-learn#13204).


  • If you want to support this project, please consider being a sponsor.
  • If you use this package in your research, please consider citing it in your paper.


  • I would like to thank Jeffrey Racine, Christoph Bergmeir, and Prabir Burman for the helpful discussion.




  title={$ hv $-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)},
  author={Zheng, Wenjie},
  journal={arXiv preprint arXiv:1910.08904},

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-0.0.5.tar.gz (9.4 kB view hashes)

Uploaded source

Built Distribution

tscv-0.0.5-py3-none-any.whl (9.3 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