The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Probabilistic discovery of time series motifs
Abstract
Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of this work were the poor scalability of the motif discovery algorithm, and the inability to discover motifs in the presence of noise.Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences. Our algorithm is probabilistic in nature, but as we show empirically and theoretically, it can find time series motifs with very high probability even in the presence of noise or "don't care" symbols. Not only is the algorithm fast, but it is an anytime algorithm, producing likely candidate motifs almost immediately, and gradually improving the quality of results over time.
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Lecture Notes in Artificial Intelligence
Brézillon, P., & Bouquet, P. (1999) · Cited by 7,473
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Durbin, R. M., Eddy, S. R., Krogh, A., & Mitchison, G. (1998) · Cited by 3,565
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Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases
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Self-citationDiscovering similar multidimensional trajectories
Vlachos, M., Kollios, G., & Gunopulos, D. (2003) · Proceedings 18th International Conference on Data Engineering · doi:10.1109/icde.2002.994784 · Cited by 1,468
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Efficient time series matching by wavelets
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Cited by
Time-series clustering – A decade review
Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015) · Information Systems · doi:10.1016/j.is.2015.04.007 · Cited by 1,826
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-citationTime series shapelets: a novel technique that allows accurate, interpretable and fast classification
Ye, L., & Keogh, E. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0179-5 · Cited by 406
Self-citationOrdonez, P., Armstrong, T., Oates, T., & Fackler, J. (2011) · 2011 10th International Conference on Machine Learning and Applications and Workshops · doi:10.1109/icmla.2011.46 · Cited by 8
Metadata
- Authors
- Chiu, B., Keogh, E., & Lonardi, S.
- Venue
- Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2003-08-24
- Pages
- 493-498
- OpenAlex
- W2006761268
- Cited by
- 573
- References
- 38
- FWCI
- 19.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
