Subsequence Anomaly Detection
Subsequence Anomaly Detection is a method within the field of time series anomaly detection that identifies anomalous patterns by analyzing variable-length segments of the time series, distinguishing itself from point-based or window-level approaches through its focus on detecting irregular temporal subsequences that deviate significantly from normal behavioral motifs.
68
Publications
5.1K
Citations
191
Authors
81
Institutions
Publications per year
2017–2026
41
68
Anomaly detection in time series
Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock · Proceedings of the VLDB Endowment · 2022 · 503 citations
Stream Monitoring under the Time Warping Distance
Yasushi Sakurai, Christos Faloutsos, Masashi Yamamuro · 2007 · 227 citations
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191
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81
University of California, Riverside
Riverside, United States
8 h-index
28 publications
4K citations
Université Paris Cité
Paris, France
5 h-index
17 publications
904 citations

Ames Research Center
Mountain View, United States
3 h-index
4 publications
276 citations
Zhejiang Gongshang University
Hangzhou, China
3 h-index
5 publications
378 citations
Laboratoire Informatique Paris Descartes
Paris, France
3 h-index
10 publications
544 citations
Michigan State University
East Lansing, United States
3 h-index
6 publications
358 citations
National Taiwan University of Science and Technology
Taipei, Taiwan
3 h-index
7 publications
169 citations
LIP6
Paris, France
3 h-index
9 publications
448 citations
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PVLDB · 7 publications · 749 citations
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6 publications · 188 citations
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2 publications · 64 citations
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2 publications · 44 citations
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2 publications · 315 citations
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