Batch Anomaly Detection
Batch Anomaly Detection is a time series anomaly detection approach that processes data in fixed, non-overlapping segments to identify deviations from normal patterns within each batch, distinguishing itself from streaming or online methods by operating on complete, accumulated datasets rather than continuous, real-time inputs.
36
Publications
2.2K
Citations
116
Authors
69
Institutions
Publications per year
2017–2026
22
36
1–5 of 36
116
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University of Electronic Science and Technology of China, China
1 publications · 54 citations
1
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1
h-index
1
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1–5 of 116
69
Pohang University of Science and Technology
Pohang, South Korea
2 h-index
3 publications
554 citations
University of Potsdam
Potsdam, Germany
2 h-index
3 publications
122 citations

McMaster University
Hamilton, Canada
2 h-index
4 publications
464 citations

University of Huddersfield
Huddersfield, United Kingdom
1 h-index
1 publications
10 citations

Hasselt University
Hasselt, Belgium
1 h-index
1 publications
28 citations
Shanghai University of Engineering Science
Shanghai, China
1 h-index
1 publications
27 citations

SINTEF
Trondheim, Norway
1 h-index
1 publications
28 citations
Technical University of Denmark
Kongens Lyngby, Denmark
1 h-index
1 publications
20 citations
1–8 of 69
5 publications · 374 citations
5
h-index
2 publications · 218 citations
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Conference · 1 publications · 15 citations
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1 publications · 22 citations
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1 publications · 28 citations
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