  Shapelets are phase independent subsequences designed for time series
classification. We propose three adaptations to the Shapelet Transform (ST) to
capture multivariate features in multivariate time series classification. We
create a unified set of data to benchmark our work on, and compare with three
other algorithms. We demonstrate that multivariate shapelets are not
significantly worse than other state-of-the-art algorithms.

Machine Learning