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.

Installation

pip install tscv

Update

pip install tscv --upgrade

I recommend you to update it often.

Usage

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.

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)

Support

See the documentation here.

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

Contributing

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

Authors

This extension is mainly developed by me, Wenjie Zheng.

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

Acknowledgment

  • 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.

License

BSD-3-Clause

Project details


Download files

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

Filename, size & hash SHA256 hash help File type Python version Upload date
tscv-0.0.3-py3-none-any.whl (8.8 kB) Copy SHA256 hash SHA256 Wheel py3
tscv-0.0.3.tar.gz (7.4 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page