Unsupervised online change point detection in high-dimensional time series

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 user-defined parameters that need to be chosen based on the observed data, which limits their applicability to new unseen data. To address these issues, we propose an Information Gain -based method that does not require prior distributional knowledge for detecting change points and handles high-dimensional time series. The advantages of our proposed method compared to the state-of-the-art algorithms are demonstrated from theoretical basis, as well as via experiments on four synthetic and three real-world human activity datasets.