Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation
Online anomaly detection (OAD) plays a pivotal role in real-time analytics and decision-making for evolving data streams. However, existing methods often rely on costly retraining and rigid decision boundaries, limiting their ability to adapt both effectively and efficiently to concept drift in dynamic environments. To address these challenges, we propose DyMETER, a dynamic concept adaptation framework for OAD that unifies on-the-fly parameter shifting and dynamic thresholding within a single online paradigm. DyMETER first learns a static detector on historical data to capture recurring central concepts, and then transitions to a dynamic mode to adapt to new concepts as drift occurs. Spec...
| Authors | Jiaqi Zhu, Shaofeng Cai, Jie Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 April 2026 |
| Last updated at source | 21 April 2026 |
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