Multi-Scale Shapelets Discovery for Time-Series Classification

Author:

Cai Borui1,Huang Guangyan1,Xiang Yong1,Angelova Maia1,Guo Limin2,Chi Chi-Hung3

Affiliation:

1. School of Information Technology, Deakin University Victoria, 3125, Australia

2. School of Computer Science, Beijing University of Technology, Beijing, 100022, China

3. Data61, CSIRO Tasmania, 7004, Australia

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.

Funder

Australian Research Council

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Science (miscellaneous),Computer Science (miscellaneous)

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