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.

翻译：序贯变点检测在众多现实应用中具有关键作用，及时识别分布漂移可极大缓解不利后果。经典方法通常依赖于变点前后分布的参数化密度假设，限制了其在高维复杂数据流中的有效性。本文提出一种基于分数的CUSUM变点检测方法，通过注入噪声并应用去噪分数匹配来估计数据分布的分数函数。我们同时考虑了分数估计的离线和在线版本。理论分析表明，去噪分数匹配能通过有效控制注入噪声的尺度来提升检测效能。最后，我们在两个合成数据集和一个真实世界地震前兆检测任务上通过数值实验验证了所提方法的实际有效性，证明了其在挑战性场景中的优越性能。