Adaptive Anomaly Detection for Non-Stationary Time-Series: A Continual Learning Framework with Dynamic Distribution Monitoring

Non-stationary time-series data poses significant challenges for anomaly detection systems due to evolving patterns and distribution shifts that render traditional static models ineffective. This paper presents a novel continual learning framework that integrates dynamic distribution monitoring mechanisms to enable adaptive anomaly detection in non-stationary environments. The proposed framework employs a dual-module architecture consisting of a distribution drift detector and an adaptive learning component. The distribution drift detector utilizes statistical hypothesis testing to identify temporal shifts in data distributions, while the adaptive learning module employs rehearsal-based continual learning strategies with dynamic memory management to maintain model performance across evolving patterns. We introduce a hybrid loss function that balances stability and plasticity, preventing catastrophic forgetting while enabling rapid adaptation to new distributions. Experimental results demonstrate an average F1-score improvement of 11.3% over the best-performing baseline, highlighting the robustness and adaptability of the proposed framework under non-stationary conditions while maintaining computational efficiency suitable for real-time applications.