Ultra-Fast Shapelets for Time Series Classification
This work addresses efficiency bottlenecks in shapelet-based classification for researchers and practitioners dealing with long or multivariate time series, representing a significant speed improvement but incremental in method.
The paper tackles the high computational cost of discovering shapelets for time series classification by proposing Ultra-Fast Shapelets, which uses random shapelets to achieve the same prediction quality as state-of-the-art methods while being up to three orders of magnitude faster, making it feasible for long multivariate time series.
Time series shapelets are discriminative subsequences and their similarity to a time series can be used for time series classification. Since the discovery of time series shapelets is costly in terms of time, the applicability on long or multivariate time series is difficult. In this work we propose Ultra-Fast Shapelets that uses a number of random shapelets. It is shown that Ultra-Fast Shapelets yield the same prediction quality as current state-of-the-art shapelet-based time series classifiers that carefully select the shapelets by being by up to three orders of magnitudes. Since this method allows a ultra-fast shapelet discovery, using shapelets for long multivariate time series classification becomes feasible. A method for using shapelets for multivariate time series is proposed and Ultra-Fast Shapelets is proven to be successful in comparison to state-of-the-art multivariate time series classifiers on 15 multivariate time series datasets from various domains. Finally, time series derivatives that have proven to be useful for other time series classifiers are investigated for the shapelet-based classifiers. It is shown that they have a positive impact and that they are easy to integrate with a simple preprocessing step, without the need of adapting the shapelet discovery algorithm.