论文标题
用内核在线更改点检测
Online change-point detection with kernels
论文作者
论文摘要
时间序列数据中的更改点通常定义为其属性发生变化的时间瞬间。在许多多样化的应用程序中检测信用卡和保险欺诈或进入网络的多种应用程序中，检测更改点至关重要。最近，作者介绍了一种基于在线内核的更改点检测方法，基于连续时间间隔对密度比的直接估算。本文进一步研究了该算法，在不存在和存在变化点的情况下，以平均值和卑鄙的方式进行了改进并分析其行为。这些理论分析通过蒙特卡洛模拟验证。通过对现实世界数据的实验进行了实验，并将算法的检测性能与最先进的方法进行了比较。
Change-points in time series data are usually defined as the time instants at which changes in their properties occur. Detecting change-points is critical in a number of applications as diverse as detecting credit card and insurance frauds, or intrusions into networks. Recently the authors introduced an online kernel-based change-point detection method built upon direct estimation of the density ratio on consecutive time intervals. This paper further investigates this algorithm, making improvements and analyzing its behavior in the mean and mean square sense, in the absence and presence of a change point. These theoretical analyses are validated with Monte Carlo simulations. The detection performance of the algorithm is illustrated through experiments on real-world data and compared to state of the art methodologies.
