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.
Installation
pip install tscv
Usage
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
.
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)
Contributing
- Report bugs in the issue tracker
- Express your use cases in the issue tracker
Support
Acknowledgments
- I would like to thank Jeffrey Racine and Christoph Bergmeir for the helpful discussion.
License
BSD-3-Clause
Citation
@article{zheng2019hv,
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},
year={2019}
}
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