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Extracting diverse-shapelets for early classification on time series

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Abstract

In recent years, early classification on time series has become increasingly important in time-sensitive applications. Existing shapelet based methods still cannot work well on this problem. First, the effectiveness of traditional shapelet based methods would be influenced by the number of shapelet candidates. Second, it is difficult for previous methods to obtain diverse shapelets in shapelet selection. In this paper, we propose an Improved Early Distinctive Shapelet Classification method named IEDSC. We first present a new method to more precisely measure the similarity between time series, which takes into account of the relative trend of time series. Second, in shapelet extraction, we propose a pruning technique to reduce the number of shapelets by predicting the starting positions of shapelets with good quality. In addition, a new shapelet selection method is also proposed to remove the similar shapelets, so as to maintain the diversity of shapelets. Finally, the experimental results on 16 benchmark datasets show that the proposed method outperforms state-of-the-art for early classification on time series.

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Acknowledgment

The authors would like to thank Prof. Eamonn Keogh and all the people who have contributed to the UCR time series classification archive for their selfless work. We also thank the anonymous reviewers for their valuable advice.

The work is supported by the National Natural Science Foundation of China (No. 61702468), Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (no. KLIGIP-2018B03) and the Zhejiang Provincial Natural Science Foundation of China (No. LZ18F020001).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, China

    Wenhe Yan & Guiling Li

  2. Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China

    Guiling Li

  3. Department of Computer Science and Engineering, Shaoxing University, Shaoxing, China

    Zongda Wu

  4. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China

    Senzhang Wang

  5. Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

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  1. Wenhe Yan
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  2. Guiling Li
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  3. Zongda Wu
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  4. Senzhang Wang
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  5. Philip S. Yu
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Correspondence to Guiling Li.

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Yan, W., Li, G., Wu, Z. et al. Extracting diverse-shapelets for early classification on time series. World Wide Web 23, 3055–3081 (2020). https://doi.org/10.1007/s11280-020-00820-z

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