Welcome to GENDIS’s documentation!

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.

This repository contains an implementation of GENDIS, an algorithm that searches for a set of shapelets in a genetic fashion. The algorithm is insensitive to its parameters (such as population size, crossover and mutation probability, …) and can quickly extract a small set of shapelets that is able to achieve predictive performances similar (or better) to that of other shapelet techniques.