Multi-Scale Shapelets Discovery for Time-Series Classification

Borui Cai, Guangyan Huang, Yong Xiang, Maia Angelova, Limin Guo, Chi Hung Chi

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)721-739
Number of pages19
JournalInternational Journal of Information Technology and Decision Making
Issue number3
Publication statusPublished - 1 May 2020

Bibliographical note

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© 2020 World Scientific Publishing Company.


  • classification
  • shapelets
  • Time-series


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