Unsupervised online change point detection in high-dimensional time series

Abstract

A critical problem in time series analysis is change point detection, which identifies the times when the underlying distribution of a time series abruptly changes. However, several shortcomings limit the use of some existing techniques in real-world applications. First, several change point detection techniques are offline methods, where the whole time series needs to be stored before change point detection can be performed. These methods are not applicable to streaming time series. Second, most techniques assume that the time series is low-dimensional and hence have problems handling high-dimensional time series, where not all dimensions may cause the change. Finally, most methods require ..