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Time series shapelets: a novel technique that allows accurate, interpretable and fast classification
Open accessAbstract
Classification of time series has been attracting great interest over the past decade. While dozens of techniques have been introduced, recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems, especially for large-scale datasets. While this may be considered good news, given the simplicity of implementing the nearest neighbor algorithm, there are some negative consequences of this. First, the nearest neighbor algorithm requires storing and searching the entire dataset, resulting in a high time and space complexity that limits its applicability, especially on resource-limited sensors. Second, beyond mere classification accuracy, we often wish to gain some insight into the data and to make the classification result more explainable, which global characteristics of the nearest neighbor cannot provide. In this work we introduce a new time series primitive, time series shapelets, which addresses these limitations. Informally, shapelets are time series subsequences which are in some sense maximally representative of a class. We can use the distance to the shapelet, rather than the distance to the nearest neighbor to classify objects. As we shall show with extensive empirical evaluations in diverse domains, classification algorithms based on the time series shapelet primitives can be interpretable, more accurate, and significantly faster than state-of-the-art classifiers.
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“poor scalability, high intractability, and low interpretability and explainability [24,25].”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [25]
Experimental — accuracy not yet measuredIn the citing paper
“Ye and Keogh (2011) identified that the most important shapelet for the Gun/NoGun classification occurs when the actor's arm is lowered (about 120 on the horizontal axis in Figure 13).”
From Deep learning for time series classification: a review (2019) · cites this paper as Ye and Keogh (2011)
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“Hills et al. (2014) introduced a shapelet transformation based approach that discovered shapelets that are similar to the ones identified by Ye and Keogh (2011).”
From Deep learning for time series classification: a review (2019) · cites this paper as Ye and Keogh (2011)
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Metadata
- Authors
- Ye, L., & Keogh, E.
- Affiliations
- University of California, Riverside (US)
- Corresponding author
- Ye, Lexiang
- ORCID
- Keogh, Eamonn
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2010-06-18
- Volume
- 22
- Issue
- 1-2
- Pages
- 149-182
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2030863907
- Cited by
- 406
- References
- 28
- Citations by year
- 2026: 22 · 2025: 29 · 2024: 42 · 2023: 47 · 2022: 43 · 2021: 43 · 2020: 32 · 2019: 28 · 2018: 31 · 2017: 18 · 2016: 19 · 2015: 15 · 2014: 18 · 2013: 12 · 2012: 6
- FWCI
- 9.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Data Mining and Knowledge Discovery (publishedVersion, cc-by-nc)
- Funders
- National Science Foundation (0803410, 0808770)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- time series classification, interpretable classification, nearest neighbor classification, large-scale datasets, resource-constrained sensors, classification speed
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