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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

Installation

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

git clone https://github.com/WenjieZ/TSCV.git tscv
mkdir YOURPROJECT/tscv
cp tscv/split.py YOURPROJECT/tscv/split.py
cp tscv/__init__.py YOURPROJECT/tscv/__init__

YOURPROJECT is the name of your project folder.

Usage

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.

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.

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


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