Probabilistic Anomaly Detection
Probabilistic Anomaly Detection is a methodological framework within anomaly detection in time series that characterizes normal behavior through probabilistic models and identifies anomalies as time points where the observed data exhibit significantly low likelihood under the estimated probability distribution.
273
Publications
16.6K
Citations
862
Authors
380
Institutions
Publications per year
2017–2026
113
273
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Ya Su, Youjian Zhao, Chenhao Niu et al. · 2019 · 1.5K citations
Anomaly Detection in Streams with Extreme Value Theory
Alban Siffer, Pierre-Alain Fouque, Alexandre Termier et al. · 2017 · 517 citations · Full text
Computing, Finance, Data Mining +12
Anomaly Detection over Noisy Data Using Learned Probability Distributions
Eleazar Eskin · 2000 · 489 citations · Full text
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380
University of Illinois Urbana-Champaign
Urbana, United States
11 h-index
17 publications
486 citations

Cornell University
Ithaca, United States
5 h-index
7 publications
107 citations

Xidian University
Xi'an, China
4 h-index
14 publications
624 citations

UNSW Sydney
Sydney, Australia
4 h-index
10 publications
1.4K citations
University of California, Davis
Davis, United States
4 h-index
6 publications
272 citations
Ben-Gurion University of the Negev
Beersheba, Israel
4 h-index
6 publications
114 citations

Harbin Institute of Technology
Harbin, China
4 h-index
18 publications
384 citations
University of Connecticut
Storrs, United States
4 h-index
5 publications
195 citations
1–8 of 380
Repository · 9 publications · 277 citations
9
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7 publications · 260 citations
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4 publications · 149 citations
4
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4 publications · 169 citations
4
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MSSP · 4 publications · 151 citations
4
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