Yingxin Ou

You are already at the latest version Business risk prediction tasks such as fraud detection, credit default prediction, and equipment failure forecasting face two fundamental challenges simultaneously: severe class imbalance where anomalous events are extremely rare, and distribution shift where data patterns evolve over time due to changing business conditions or adversarial behavior.

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.

Submitted: 24 December 2025 You are already at the latest version Time-series anomaly detection faces significant challenges when dealing with imbalanced data distributions, distribution shifts, and heterogeneous feature types. Traditional supervised methods struggle due to limited labeled anomaly samples, while unsupervised approaches often produce high false positive rates.