In the time series classification domain, shapelets are small subseries that are discriminative for a certain class. It has been shown that by projecting the original dataset to a distance space, where each axis corresponds to the distance to a certain shapelet, classifiers are able to achieve state-of-the-art results on a plethora of datasets. In order to find these shapelets, the current state-of-the-art (in terms of predictive performance) performs a brute-force search that quickly becomes intractable for larger datasets.

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Therefore, we propose a genetic algorithm that searches for an entire set of shapelets directly. This results in a more scalable algorithm that is competitive to the current state-of-the-art. Moreover, the number of shapelets needed to achieve this competitive performance is several orders of magnitude smaller than the current sota.

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The implementation follows the sklearn interface. You can just simply use `fit` and `transform` methods. There are docs.

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You can find the code (with an example notebook and tutorial) on Github.