  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.
