Transformer-based Anomaly Detection
Transformer-Based Anomaly Detection is a method for identifying anomalous patterns in time series data by leveraging the self-attention mechanism of transformer architectures to model long-range temporal dependencies more effectively than traditional sequential models.
157
Publications
12.9K
Citations
644
Authors
251
Institutions
Publications per year
2017–2026
152
157
Transformers in Time Series: A Survey
Qingsong Wen, Tian Zhou, Chaoli Zhang et al. · 2023 · 999 citations · Full text
Computing, Data Science, Forecasting +16
Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings · Proceedings of the VLDB Endowment · 2022 · 829 citations
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Tsinghua University
Beijing, China
7 h-index
21 publications
1.6K citations

Shanghai Jiao Tong University
Shanghai, China
6 h-index
21 publications
2.5K citations

Harbin Institute of Technology
Harbin, China
6 h-index
21 publications
1.6K citations
Korea University
Seoul, South Korea
4 h-index
10 publications
1K citations

Chinese Academy of Sciences
Beijing, China
4 h-index
10 publications
475 citations
University of Udine
Udine, Italy
3 h-index
12 publications
1.6K citations

Xidian University
Xi'an, China
3 h-index
11 publications
1K citations

Huazhong University of Science and Technology
Wuhan, China
3 h-index
6 publications
388 citations
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Repository · 11 publications · 2.2K citations
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10 publications · 648 citations
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5 publications · 415 citations
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Conference · 3 publications · 318 citations
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3 publications · 75 citations
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