Shapelets are time series subsequences that are maximally representative of a class [1].
MARS' shapelets are:
- Multivariate: shapelets have the same number of dimensions of the time series provided. The distance between a shapelet and a time series is calculated as the sum of the minimum distance for each dimension.
- Asynchronous: (by default) the different dimension of the shapelets can be extracted from different timestamps and they are compared with each timestamp of the time series.
- Random: shapelets are extracted randomly for the sake of computation time.
pip install git+https://github.com/bianchimario/MARS
- numpy
- scipy
- random
- awkward
[1] Ye, Lexiang, and Eamonn Keogh. ‘Time Series Shapelets: A Novel Technique That Allows Accurate, Interpretable and Fast Classification’. Data Mining and Knowledge Discovery 22, no. 1–2 (January 2011): 149–82. https://doi.org/10.1007/s10618-010-0179-5.
[2] Awkward library