Multi-Scale Shapelets Discovery for Time-Series Classification

Borui Cai (), Guangyan Huang (), Yong Xiang (), Maia Angelova (), Limin Guo () and Chi-Hung Chi ()
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Borui Cai: School of Information Technology, Deakin University Victoria, 3125, Australia
Guangyan Huang: School of Information Technology, Deakin University Victoria, 3125, Australia
Yong Xiang: School of Information Technology, Deakin University Victoria, 3125, Australia
Maia Angelova: School of Information Technology, Deakin University Victoria, 3125, Australia
Limin Guo: School of Computer Science, Beijing University of Technology, Beijing, 100022, China
Chi-Hung Chi: Data61, CSIRO Tasmania, 7004, Australia

International Journal of Information Technology & Decision Making (IJITDM), 2020, vol. 19, issue 03, 721-739

Abstract: Shapelets are subsequences of time-series that represent local patterns and can improve the accuracy and the interpretability of time-series classification. The major task of time-series classification using shapelets is to discover high quality shapelets. However, this is challenging since local patterns may have various scales/lengths rather than a unified scale. In this paper, we resolve this problem by discovering shapelets with multiple scales. We propose a novel Multi-Scale Shapelet Discovery (MSSD) algorithm to discover expressive multi-scale shapelets by extending initial single-scale shapelets (i.e., shapelets with a unified scale). MSSD adopts a bi-directional extension process and is robust to extend single-shapelets obtained by different methods. A supervised shapelet quality measurement is further developed to qualify the extension of shapelets. Comprehensive experiments conducted on 25 UCR time-series datasets show that multi-scale shapelets discovered by MSSD improve classification accuracy by around 10% (in average), compared with single-scale shapelets discovered by counterpart methods.

Keywords: Time-series; shapelets; classification (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0219622020500133

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