Training Neural Networks for Sequential Change-Point Detection

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 net-works. To be specific, our approach is training neural net-works 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.
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