The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Time series shapelets: a new primitive for data mining
Abstract
Classification of time series has been attracting great interest over the past decade. Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems. 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 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.
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Cites
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
Cited by
A Review on Outlier/Anomaly Detection in Time Series Data
Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2022) · ACM Computing Surveys · doi:10.1145/3444690 · Cited by 1,102
Searching and mining trillions of time series subsequences under dynamic time warping
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., & Keogh, E. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339576 · Cited by 1,047
Self-citationThe BOSS is concerned with time series classification in the presence of noise
Schäfer, P. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0377-7 · Cited by 560
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
Time series classification with ensembles of elastic distance measures
Lines, J., & Bagnall, A. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0361-2 · Cited by 522
Metadata
- Authors
- Ye, L., & Keogh, E.
- Venue
- Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2009-06-28
- Pages
- 947-956
- OpenAlex
- W2029438113
- Cited by
- 987
- References
- 1
- FWCI
- 19.49
- Type
- conference paper
- Language
- en
- Open access
- closed
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