We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to flexible algorithms suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets.

翻译：我们提出了一种新颖的在线变点检测方法。该方法扩展了最大化变点前后分布数据点间差异度量的思想，从而衍生出适用于参数与非参数场景的灵活算法。我们证明了该过程的平均运行长度和预期检测延迟的非渐近界。通过合成数据集与真实世界数据集的数值实验验证了算法的有效性。