A Fast Shapelet Discovery Algorithm Based on Important Data Points

Cun Ji, Chao Zhao, Li Pan, Shijun Liu, Chenglei Yang and Lei Wu
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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

International Journal of Web Services Research (IJWSR), 2017, vol. 14, issue 2, 67-80

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.

Date: 2017
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