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Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era.
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A Systematic Framework to Generate Invariants for Anomaly Detection in Industrial Control Systems. In 26th Annual Network and Distributed System Security Symposium, NDSS 2019, San Diego, California, USA, February 24-27, 2019 . The Internet Society
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Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery; Data Mining (Virtual Event, Singapore) (KDD ’21) . Association for Computing Machinery, New York, NY, USA, 2485–2494
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GAN-Based Anomaly Detection for Multivariate Time Series Using Polluted Training Set
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Cheng Feng and Pengwei Tian. 2021 · 2021
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Towards a Rigorous Evaluation of Time-series Anomaly Detection
Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon. 2021 · 2021
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Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 3220–3230
Zhihan Li, Youjian Zhao, Jiaqi Han, Ya Su, Rui Jiao, Xidao Wen, and Dan Pei. 2021 · 2021
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ELITE: Robust Deep Anomaly Detection with Meta Gradient. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 2174–2182
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TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
Shreshth Tuli, Giuliano Casale, and Nicholas R Jennings. 2022 · 2022
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