Jiaqi Zhu, Shaofeng Cai, Jie Chen, Fang Deng, Beng Chin Ooi, Wenqiao Zhang

Researchers propose DyMETER, a framework for online anomaly detection that adapts to concept drift in streaming data without costly retraining. The system combines a static detector trained on historical data with dynamic parameter shifting via hypernetwork and adaptive threshold optimization to handle evolving patterns. This addresses a core limitation in real-time analytics where rigid decision boundaries fail as data distributions shift over time.