Establish theoretical guarantees for outlier detection in non-stationary data streams

Establish rigorous theoretical guarantees for outlier detection in non-stationary data streams, particularly for approaches based on sliding-window techniques, ensemble methods, and data weighting, to characterize their performance and error control under distribution shift.

Background

The paper studies one-pass streaming outlier detection under non-stationarity and proposes SONAR, an SGD-based variant of OCSVM with strong convexity and changepoint detection. In surveying prior work on outlier detection in changing environments, the authors note that many practical approaches—such as sliding-window methods, ensembles, and data weighting—lack rigorous theoretical analysis in non-stationary settings.

This gap motivates the development of formal guarantees that can bound Type I and Type II errors or otherwise quantify performance when distributions shift over time. The authors’ method provides such guarantees for their specific approach, but the broader landscape of non-stationary outlier detection methods remains largely unanalyzed.