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In the research area of time series classification, the ensemble shapelettransform algorithm is one of state-of-the-art algorithms for classification.However, its high time complexity is an issue to hinder its application sinceits base classifier shapelet transform includes a high time complexity of adistance calculation and shapelet selection. Therefore, in this paper weintroduce a novel algorithm, i.e. short isometric shapelet transform, whichcontains two strategies to reduce the time complexity. The first strategy ofSIST fixes the length of shapelet based on a simplified distance calculation,which largely reduces the number of shapelet candidates as well as speeds upthe distance calculation in the ensemble shapelet transform algorithm. Thesecond strategy is to train a single linear classifier in the feature spaceinstead of an ensemble classifier. The theoretical evidences of these twostrategies are presented to guarantee a near-lossless accuracy under somepreconditions while reducing the time complexity. Furthermore, empiricalexperiments demonstrate the superior performance of the proposed algorithm.

Use Short Isometric Shapelets to Accelerate Binary Time Series Classification