Missing Data Robust Anomaly Detection
Missing Data Robust Anomaly Detection is an anomaly detection framework designed to reliably identify anomalous patterns in time series data while maintaining detection accuracy and model stability in the presence of significant proportions of missing data, distinguishing it from standard anomaly detection methods that assume complete or fully imputed data sequences.
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1.3K
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74
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50
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2017–2026
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Yin Zhang⋆, Zihui Ge, Albert Greenberg et al. · 2005 · 214 citations
Yin Zhang, Zihui Ge, Albert Greenberg et al. · 2005 · 156 citations
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Sorbonne Université
Paris, France
2 h-index
2 publications
68 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
4 publications
150 citations

Xi'an Jiaotong University
Xi'an, China
2 h-index
6 publications
180 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
2 h-index
3 publications
623 citations

Zhejiang University of Technology
Hangzhou, China
2 h-index
5 publications
947 citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
1 h-index
1 publications
25 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
1 publications
20 citations

Huazhong University of Science and Technology
Wuhan, China
1 h-index
3 publications
117 citations
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2 publications · 59 citations
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PVLDB · 1 publications · 102 citations
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1 publications · 29 citations
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1 publications · 27 citations
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1 publications · 25 citations
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