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Short isometric shapelet transform for binary time series classification

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Abstract

In the research area of time series classification, the ensemble shapelet transform algorithm is one of the state-of-the-art algorithms for classification. However, its high time complexity is an issue to hinder its application since its base classifier shapelet transform includes a high time complexity of a distance calculation and shapelet selection. Therefore, in this paper we introduce a novel algorithm, i.e., short isometric shapelet transform (SIST), which contains two strategies to reduce the time complexity. The first strategy of SIST fixes the length of shapelet based on a simplified distance calculation, which largely reduces the number of shapelet candidates as well as speeds up the distance calculation in the ensemble shapelet transform algorithm. The second strategy is to train a single linear classifier in the feature space instead of an ensemble classifier. The theoretical evidence of these two strategies is presented to guarantee a near-lossless accuracy under some preconditions while reducing the time complexity. Furthermore, empirical experiments demonstrate the superior performance of the proposed algorithm.

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Acknowledgements

We thank anonymous reviewers for their very useful comments and suggestions. This research is supported in part by the National Key Research and Development Program of China under Grant No. 2016YFB1000905, the National Natural Science Foundation of China under Grant No. 91746209, and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China

    Weibo Shu, Yaqiang Yao, Shengfei Lyu, Jinlong Li & Huanhuan Chen

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  1. Weibo Shu
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  2. Yaqiang Yao
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  3. Shengfei Lyu
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  4. Jinlong Li
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  5. Huanhuan Chen
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Correspondence to Huanhuan Chen.

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Shu, W., Yao, Y., Lyu, S. et al. Short isometric shapelet transform for binary time series classification. Knowl Inf Syst 63, 2023–2051 (2021). https://doi.org/10.1007/s10115-021-01583-3

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