Cited by 406Time Series Analysis and ForecastingImage Retrieval and Classification TechniquesData Mining Algorithms and Applications

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Time series shapelets: a novel technique that allows accurate, interpretable and fast classification

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Ye, Lexiang, Keogh, Eamonn·2011·Data Mining and Knowledge Discovery

DOI 10.1007/s10618-010-0179-5

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Abstract

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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Authors: Ye, L., & Keogh, E.
Affiliations: University of California, Riverside (US)
Corresponding author: Ye, Lexiang
ORCID: Keogh, Eamonn
Venue: Data Mining and Knowledge Discovery
Publisher: Springer Science and Business Media LLC
Published: 2010-06-18
Volume: 22
Issue: 1-2
Pages: 149-182
ISSN: 1384-5810 (ISSN-L)
1573-756X
DOI: 10.1007/s10618-010-0179-5
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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Time series shapelets: a novel technique that allows accurate, interpretable and fast classification

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