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A Fast Shapelet Discovery Algorithm Based on Important Data Points

Author

Listed:
  • Cun Ji

    (School of Computer Science and Technology, Shandong University, Jinan, China)

  • Chao Zhao

    (School of Computer Science and Technology, Shandong University, Jinan, China)

  • Li Pan

    (School of Computer Science and Technology, Shandong University, Jinan, China & Engineering Research Center of Digital Media Technology, Ministry of Education, Jinan, China)

  • Shijun Liu

    (School of Computer Science and Technology, Shandong University, Jinan, China & Engineering Research Center of Digital Media Technology, Ministry of Education, Jinan, China)

  • Chenglei Yang

    (School of Computer Science and Technology, Shandong University, Jinan, China & Engineering Research Center of Digital Media Technology, Ministry of Education, Jinan, China)

  • Lei Wu

    (School of Computer Science and Technology, Shandong University, Jinan, China & Engineering Research Center of Digital Media Technology, Ministry of Education, Jinan, China)

Abstract

Time series classification (TSC) has attracted significant interest over the past decade. A shapelet is one fragment of a time series that can represent class characteristics of the time series. A classifier based on shapelets is interpretable, more accurate, and faster. However, the time it takes to find shapelets is enormous. This article will propose a fast shapelet (FS) discovery algorithm based on important data points (IDPs). First, the algorithm will identify IDPs. Next, the subsequence containing one or more IDPs will be selected as a candidate shapelet. Finally, the best shapelets will be selected. Results will show that the proposed algorithm reduces the shapelet discovery time by approximately 14.0% while maintaining the same level of classification accuracy rates.

Suggested Citation

  • Cun Ji & Chao Zhao & Li Pan & Shijun Liu & Chenglei Yang & Lei Wu, 2017. "A Fast Shapelet Discovery Algorithm Based on Important Data Points," International Journal of Web Services Research (IJWSR), IGI Global, vol. 14(2), pages 67-80, April.
    • Handle: RePEc:igg:jwsr00:v:14:y:2017:i:2:p:67-80
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