Efficient Shapelet Discovery for Time Series Classification

Guozhong Li*, Byron Choi, Jianliang Xu, Sourav S Bhowmick, Kwok Pan Chun, Grace Lai Hung Wong

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

22 Citations (Scopus)


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. Specifically, bspcover generates abundant candidates via Symbolic Aggregate approXimation with sliding window, then prunes identical and highly similar candidates via Bloom filters, and similarity matching, respectively. We next propose a p-Cover algorithm to efficiently determine discriminative shapelet candidates that maximally represent each time-series class. Finally, any existing shapelet learning method can be adopted to build a classification model. We have conducted extensive experiments with well-known 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
Pages (from-to)1149-1163
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number3
Publication statusPublished - Mar 2022

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Accuracy
  • Efficiency
  • Shapelet discovery
  • Time series classification


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