A Shapelet Transform for Multivariate Time Series Classification
This work addresses classification of multivariate time series, but it is incremental as it adapts an existing method without major breakthroughs.
The authors tackled multivariate time series classification by adapting the Shapelet Transform to capture multivariate features, and found that their method is not significantly worse than other state-of-the-art algorithms.
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.