Sequential Change Point Detection via Denoising Score Matching
This work addresses timely detection of distributional shifts for applications like monitoring systems, though it appears incremental as it builds on existing score-based and CUSUM frameworks.
The paper tackled the problem of sequential change-point detection in high-dimensional, complex data streams by proposing a score-based CUSUM method using denoising score matching, and demonstrated its effectiveness through numerical experiments on synthetic datasets and a real-world earthquake precursor detection task.
Sequential change-point detection plays a critical role in numerous real-world applications, where timely identification of distributional shifts can greatly mitigate adverse outcomes. Classical methods commonly rely on parametric density assumptions of pre- and post-change distributions, limiting their effectiveness for high-dimensional, complex data streams. This paper proposes a score-based CUSUM change-point detection, in which the score functions of the data distribution are estimated by injecting noise and applying denoising score matching. We consider both offline and online versions of score estimation. Through theoretical analysis, we demonstrate that denoising score matching can enhance detection power by effectively controlling the injected noise scale. Finally, we validate the practical efficacy of our method through numerical experiments on two synthetic datasets and a real-world earthquake precursor detection task, demonstrating its effectiveness in challenging scenarios.