Efficient shapelet discovery for time series classification (extended abstract)

Guozhong Li, Byron Choi, Jianliang Xu, Sourav S. Bhowmick, Kwok Pan Chun, Grace L.H. Wong

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


Time-series shapelets are discriminative subsequences, recently found effective for time series classification (TSC). It is evident that the quality of shapelets is crucial to the accuracy of TSC. However, major research has focused on building accurate models from some shapelet candidates. To determine such candidates, existing studies are surprisingly simple, e.g., enumerating subsequences of some fixed lengths, or randomly selecting some subsequences as shapelet candidates. The major bulk of computation is then on building the model from the candidates. In this paper, we propose a novel efficient shapelet discovery method, called BSPCOVER, to discover a set of high-quality shapelet candidates for model building. We have conducted extensive experiments with well-known UCR time-series datasets and representative state-of-the-art methods. Results show that BSPCOVER speeds up the state-of-the-art methods by more than 70 times, and the accuracy is often comparable to or higher than existing works.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
Number of pages2
ISBN (Electronic)9781728191843
ISBN (Print)9781728191850
Publication statusPublished - Apr 2021
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: 19 Apr 202122 Apr 2021

Publication series

NameProceedings of IEEE International Conference on Data Engineering (ICDE)
ISSN (Print)1063-6382
ISSN (Electronic)2375-026X


Conference37th IEEE International Conference on Data Engineering, ICDE 2021
CityVirtual, Chania
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems


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