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.
Original language | English |
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Pages (from-to) | 721-739 |
Number of pages | 19 |
Journal | International Journal of Information Technology and Decision Making |
Volume | 19 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 May 2020 |
Bibliographical note
Publisher Copyright:© 2020 World Scientific Publishing Company.
Keywords
- classification
- shapelets
- Time-series