Rethinking Time Series Anomaly Detection from a Dynamic Perspective: Temporal-Frequency-Curvature Fusion
Published in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Research Track), 2026
Hang Cui, Zexin Wang, Changhua Pei, Juncheng Hu, Haotian Si, Quan Zhou, Cenjie Hu, Jingjing Li, Dan Pei, and Gaogang Xie. Rethinking Time Series Anomaly Detection from a Dynamic Perspective: Temporal-Frequency-Curvature Fusion. KDD 2026 Research Track.
Recommended citation: Hang Cui, Zexin Wang, Changhua Pei, Juncheng Hu, Haotian Si, Quan Zhou, Cenjie Hu, Jingjing Li, Dan Pei, and Gaogang Xie. Rethinking Time Series Anomaly Detection from a Dynamic Perspective: Temporal-Frequency-Curvature Fusion. KDD 2026 Research Track.
Download Paper