ABSTRACT

Online change point detection methods monitor changes in the distribution of a data stream. This article discusses two non-parametric online change detection methods based on the energy statistics and Mahalanobis depth. To apply the energy statistic, we use sliding-window algorithm with efficient training and updating procedures. For Mahalanobis depth, we propose an algorithm to train the threshold with desired protective ability against false alarms and discuss factors that have an influence on the threshold. Numerical studies evaluate and compare the performance of the proposed models with three existing methods to detect changes in the mean and variability of a data stream. The methods are applied to detecting changes in the flowing volume of the Mississippi River.

 抽象的

在线变化点检测方法监视数据流分布的变化。本文讨论了两种基于能量统计和马氏深度的非参数在线变化检测方法。为了应用能量统计，我们使用具有高效训练和更新程序的滑动窗口算法。对于马哈拉诺比斯深度，我们提出了一种算法来训练具有所需的防误报保护能力的阈值，并讨论了影响阈值的因素。数值研究评估并比较了所提出模型与三种现有方法的性能，以检测数据流平均值和变异性的变化。该方法用于检测密西西比河流量的变化。