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Multi-Scale Shapelets Discovery for Time-Series Classification

journal contribution
posted on 2020-01-01, 00:00 authored by Borui Cai Borui, Guangyan HuangGuangyan Huang, Yong XiangYong Xiang, Maia Angelova TurkedjievaMaia Angelova Turkedjieva, Limin Guo, Chi-Hung Chi
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.

History

Journal

International Journal of Information Technology & Decision Making

Volume

19

Pagination

1 - 19

Publisher

World Scientific Publishing

Location

Singapore

ISSN

0219-6220

eISSN

1793-6845

Language

English

Publication classification

C1 Refereed article in a scholarly journal