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.

• A Medium post providing some motivation and explaining the cross-validation algorithms implemented here in more detail.
• Advances in financial machine learning by Marcos Lopez de Prado. An excellent book that inspired this package.
• Github repository

timeseriescv can be installed using pip:

>>> pip install timeseriescv

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.

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:

• The 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.
• To stay as close to the scikit-learn API as possible, this data is passed as separate parameters. But in order to ensure that they are properly aligned, 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.