RoS-Guard: Robust and Scalable Online Change Detection with Delay-Optimal Guarantees
This work addresses efficiency and robustness issues in online change detection for applications like power system monitoring, offering incremental improvements over existing methods.
The paper tackles the problem of online change detection in streaming data by proposing RoS-Guard, a robust algorithm for linear systems with uncertainty, which achieves significant computational speedup in large-scale scenarios through GPU acceleration.
Online change detection (OCD) aims to rapidly identify change points in streaming data and is critical in applications such as power system monitoring, wireless network sensing, and financial anomaly detection. Existing OCD methods typically assume precise system knowledge, which is unrealistic due to estimation errors and environmental variations. Moreover, existing OCD methods often struggle with efficiency in large-scale systems. To overcome these challenges, we propose RoS-Guard, a robust and optimal OCD algorithm tailored for linear systems with uncertainty. Through a tight relaxation and reformulation of the OCD optimization problem, RoS-Guard employs neural unrolling to enable efficient parallel computation via GPU acceleration. The algorithm provides theoretical guarantees on performance, including expected false alarm rate and worst-case average detection delay. Extensive experiments validate the effectiveness of RoS-Guard and demonstrate significant computational speedup in large-scale system scenarios.