Detecting an abrupt distributional shift of a data stream, known as change-point detection, is a fundamental problem in statistics and machine learning. We introduce a novel approach for online change-point detection using neural networks. To be specific, our approach is training neural networks to compute the cumulative sum of a detection statistic sequentially, which exhibits a significant change when a change-point occurs. We demonstrated the superiority and potential of the proposed method in detecting change-point using both synthetic and real-world data.

翻译：检测数据流中突然的分布变化（称为变化点检测）是统计学和机器学习中的一个基本问题。我们提出了一种使用神经网络进行在线变化点检测的新方法。具体而言，我们的方法训练神经网络依次计算检测统计量的累积和，当变化点出现时，该累积和会表现出显著变化。我们通过合成数据和真实世界数据展示了所提出方法在检测变化点方面的优越性和潜力。