We propose an algorithm for nonparametric online change point detection based on sequential score function estimation and the tracking the best expert approach. The core of the procedure is a version of the fixed share forecaster tailored to the case of infinite number of experts and quadratic loss functions. The algorithm shows promising results in numerical experiments on artificial and real-world data sets. Its performance is supported by rigorous high-probability bounds describing behaviour of the test statistic in the pre-change and post-change regimes.

翻译：本文提出一种基于序列评分函数估计与追踪最优专家方法的非参数在线变点检测算法。该算法的核心是针对无限专家数量与二次损失函数场景定制的固定份额预测器变体。在人工与真实数据集上的数值实验中，该算法展现出具有前景的性能表现。其有效性通过严格的概率上界理论得以支撑，该理论精确描述了检验统计量在变点前与变点后两种状态下的行为特征。