Anomaly Visualization
Anomaly Visualization is a time-series visualization technique that specifically highlights deviations from expected patterns by integrating visual encodings to distinguish irregular data points from normal behavior.
114
Publications
4.7K
Citations
376
Authors
144
Institutions
Publications per year
2017–2026
38
Parents
114
1–5 of 114
376
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h-index
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h-index
5
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1–5 of 376
144
Tongji University
Shanghai, China
5 h-index
9 publications
259 citations
University of Konstanz
Konstanz, Germany
5 h-index
13 publications
711 citations
University of California, Riverside
Riverside, United States
5 h-index
21 publications
1.6K citations
University of California, Davis
Davis, United States
5 h-index
8 publications
192 citations

University of Skövde
Skövde, Sweden
5 h-index
11 publications
363 citations

Chinese Academy of Sciences
Beijing, China
4 h-index
7 publications
101 citations
IBM (United States)
Armonk, United States
4 h-index
10 publications
883 citations
Central Michigan University
Mount Pleasant, United States
4 h-index
5 publications
78 citations
1–8 of 144
TVCG · 10 publications · 823 citations
10
h-index
3 publications · 45 citations
3
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Repository · 3 publications · 55 citations
3
h-index
2 publications · 99 citations
2
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2 publications · 53 citations
2
h-index
1–5