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.
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.
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:
- 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.
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