Anomaly Scoring
Anomaly Scoring is a computational method that quantifies the degree to which a data point in a time series deviates from expected behavior, distinguishing itself from other anomaly detection techniques by producing a continuous, interpretable score that reflects the likelihood or severity of anomalous behavior rather than yielding a binary classification.
69
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4K
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246
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112
Institutions
Publications per year
2017–2026
55
69
Deep Anomaly Detection with Deviation Networks
Guansong Pang, Chunhua Shen, Anton van den Hengel · 2019 · 439 citations · Full text
Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks
Ping Liu, Haowen Xu, Qianyu Ouyang et al. · 2020 · 204 citations
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112

The University of Adelaide
Adelaide, Australia
4 h-index
12 publications
1.6K citations

Radboud University Nijmegen
Nijmegen, The Netherlands
3 h-index
9 publications
352 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
7 publications
269 citations
National Institute for Subatomic Physics
Amsterdam, The Netherlands
2 h-index
3 publications
92 citations
Université Paris Cité
Paris, France
2 h-index
4 publications
304 citations

Nankai University
Tianjin, China
2 h-index
2 publications
273 citations
Electronics and Telecommunications Research Institute
Daejeon, South Korea
2 h-index
8 publications
236 citations
University of Chicago
Chicago, United States
2 h-index
6 publications
384 citations
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Repository · 8 publications · 410 citations
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4 publications · 142 citations
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TKDE · 2 publications · 487 citations
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1 publications · 16 citations
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AJ · 1 publications · 27 citations
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