The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
Abstract
In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. Time series discords have many uses for data mining, including improving the quality of clustering, data cleaning, summarization, and anomaly detection. Discords are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. We evaluate our work with a comprehensive set of experiments. In particular, we demonstrate the utility of discords with objective experiments on domains as diverse as Space Shuttle telemetry monitoring, medicine, surveillance, and industry, and we demonstrate the effectiveness of our discord discovery algorithm with more than one million experiments, on 82 different datasets from diverse domains.
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Cites
Distance-based outliers: algorithms and applications
Knorr, E. M., Ng, R. T., & Tucakov, V. (2000) · The VLDB Journal The International Journal on Very Large Data Bases · doi:10.1007/s007780050006 · Cited by 1,195
Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
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-citationA 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
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016) · arXiv · doi:10.48550/arxiv.1607.00148 · Cited by 819
Jin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G. I., King, I., & Pan, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3443141 · Cited by 567
Trajectory-Based Anomalous Event Detection
Piciarelli, C., Micheloni, C., & Foresti, G. L. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.2005599 · Cited by 532
A Survey on Explainable Anomaly Detection
Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139
“HOT-SAX [101] and RRA [192], to discovery anomalies, respectively.”
BackgroundFekr, A. R., Radecka, K., & Zilic, Z. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2445783 · Cited by 61
IoT data cleaning techniques: A survey
Ding, X., Wang, H., Li, G., Li, H., Li, Y., & Liu, Y. (2022) · Intelligent and Converged Networks · doi:10.23919/icn.2022.0026 · Cited by 35
Plan execution monitoring through detection of unmet expectations about action outcomes
Mendoza, J. P., Veloso, M., & Simmons, R. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139646 · Cited by 18
Ramirez, E., Wimmer, M., & Atzmueller, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-37446-4_11 · Cited by 17
Metadata
- Authors
- Keogh, E., Lin, J., & Fu, A.
- Venue
- Fifth IEEE International Conference on Data Mining (ICDM'05)
- Publisher
- IEEE
- Published
- 2006-01-05
- Pages
- 226-233
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2105510466
- Cited by
- 696
- References
- 1
- FWCI
- 25.30
- Type
- conference paper
- Language
- en
- Open access
- closed
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