提出高效鲁棒的在线变化检测算法，支持大规模系统实时预警。
RoS-Guard: Robust and Scalable Online Change Detection with Delay-Optimal Guarantees
- 通过神经网络展开实现并行计算，提升处理效率。
- 在不确定线性系统中实现低误报率与最优平均检测延迟。
- 适合电力监控、金融异常等大规模实时数据场景。
在线变化检测（OCD）旨在快速识别流式数据中的变化点，在电力系统监测、无线网络感知和金融异常检测等应用中至关重要。现有方法通常依赖精确的系统知识，但实际中受估计误差和环境变化影响难以满足。同时，现有方法在大规模系统中效率不足。为此，本文提出RoS-Guard，一种针对存在不确定性的线性系统的鲁棒且高效的OCD算法。通过紧致松弛与重构优化问题，该算法采用神经网络展开技术，借助GPU实现高效并行计算。算法提供理论保障，包括期望误报率和最坏情况下的平均检测延迟。大量实验验证了其有效性，并在大规模系统中实现显著的计算加速。
原文摘要 · Abstract (English)
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.
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