Scikit-learn style cross-validation classes for time series data
This package implements two cross-validation algorithms suitable to evaluate machine learning models based on time series
datasets where each sample is tagged with a prediction time and an evaluation time.
Resources
* `A Medium post <https://medium.com/@samuel.monnier/cross-validation-tools-for-time-series-ffa1a5a09bf9>`_ providing some motivation and explaining the cross-validation algorithms implemented here in more detail.
* `Advances in financial machine learning <https://www.wiley.com/en-us/Advances+in+Financial+Machine+Learning-p-9781119482086>`_ by Marcos Lopez de Prado. An excellent book that inspired this package.
* `Github repository <https://github.com/sam31415/timeseriescv/>`_
Installation
timeseriescv can be installed using pip:
>>> pip install timeseriescv
Content
For now the package contains two main classes handling cross-validation:
* ``PurgedWalkForwardCV``: Walk-forward cross-validation with purging.
* ``CombPurgedKFoldCV``: Combinatorial cross-validation with purging and embargoing.
Remarks concerning the API
The API is as similar to the scikit-learn API as possible. Like the scikit-learn cross-validation classes, the split
method is a generator that yields a pair of numpy arrays containing the positional indices of the samples in the train
and validation set, respectively. The main differences with the scikit-learn API are:
split method takes as arguments not only the predictor values X, but also the prediction times pred_times and the evaluation times eval_times of each sample.X, pred_times and eval_times are required to be pandas DataFrames/Series sharing the same index.Check the docstrings of the cross-validation classes for more information.