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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


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

  • GapLeavePOut
  • GapKFold
  • GapRollForward
  • gap_train_test_split

The three classes can all be passed, as the cv argument, to scikit-learn functions such as cross-validate, cross_val_score, and cross_val_predict, just like the native cross-validator classes.

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



  • I would like to thank Jeffrey Racine and Christoph Bergmeir 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},