TSI-GAN: Unsupervised Time Series Anomaly Detection Using Convolutional Cycle-Consistent Generative Adversarial Networks

Author:

Saravanan Shyam Sundar,Luo TieORCID,Van Ngo Mao

Publisher

Springer Nature Switzerland

Cited by 6 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. Explainable time series anomaly detection using masked latent generative modeling;Pattern Recognition;2024-12

2. Anomaly prediction of Internet behavior based on generative adversarial networks;PeerJ Computer Science;2024-07-23

3. Unraveling the ‘Anomaly’ in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

4. Online Error Evaluation of Capacitive Voltage Transformer Based on Improved Temporal Convolutional Network Autoencoder;2024 10th International Conference on Electrical Engineering, Control and Robotics (EECR);2024-03-29

5. 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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