2017/12/18 by Aaron Bostrom, Bostrom, Aaron, Anthony Bagnall +1
Computer Science · #Time Series Analysis and Forecasting #Anomaly Detection Techniques and Applications #Music and Audio Processing
paper · pdf · doi:10.48550/arxiv.1712.06428
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.