TSFN: an Effective Time Series Anomaly Detection Approach via Transformer-based Self-feedback Network

Author:

Wu Hongwei1,Yang Rong1,Qing Huang2,Liu Kedong3,Jiang Zhuojun1,Li Yangxi3,Zhang Hong3

Affiliation:

1. Institute of Information Engineering,Chinese Academy of Science,Beijing,China

2. China Coal Research Institute,Beijing,China

3. National Computer Network Emergency Response Technical Team,Coordination Center of China,Beijing,China

Funder

Chinese Academy of Sciences

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

IEEE

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Multi-Graph Structure Learning-based Multivariate Time Series Anomaly Detection with Extended Prior Knowledge;2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD);2024-05-08

2. Real- Time Anomaly Detection with Generative Adversarial Networks for Network Security Management;2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC);2024-01-29

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