The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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)
In the citing paper
“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)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
Martinez, A. M., & Kak, A. C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908974 · Cited by 3,249
(1994) · Knowledge Discovery and Data Mining · Cited by 3,137
Exact indexing of dynamic time warping
Keogh, E., & Ratanamahatana, C. A. (2004) · Knowledge and Information Systems · doi:10.1007/s10115-004-0154-9 · Cited by 1,962
Self-citationFast subsequence matching in time-series databases
Faloutsos, C., Ranganathan, M., & Manolopoulos, Y. (1994) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/191839.191925 · Cited by 1,712
Experiencing SAX: A novel symbolic representation of time series
Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-007-0064-z · Cited by 1,618
Self-citationQuerying and mining of time series data
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., & Keogh, E. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1454159.1454226 · Cited by 1,353
Self-citationYe, L., & Keogh, E. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557122 · Cited by 992
Self-citation
Research works printed but not linked: 1
- Koschorreck W, Werner W (eds) (1981) Facsimile edition with commentary: Kommentar zum faksimile des codex manesse: Die grosse Heidelberger Liederhandschrift
Cited by
Sorted by citations
Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342
“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).”
BackgroundMethodBagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0483-9 · Cited by 1,377
Self-citationMapping the stereotyped behaviour of freely moving fruit flies
Berman, G. J., Choi, D. M., Bialek, W., & Shaevitz, J. W. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0672 · Cited by 565
The BOSS is concerned with time series classification in the presence of noise
Schäfer, P. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0377-7 · Cited by 564
Classification of time series by shapelet transformation
Hills, J., Lines, J., Baranauskas, E., Mapp, J., & Bagnall, A. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-013-0322-1 · Cited by 538
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
