  Detecting relevant changes in dynamic time series data in a timely manner is
crucially important for many data analysis tasks in real-world settings. Change
point detection methods have the ability to discover changes in an unsupervised
fashion, which represents a desirable property in the analysis of unbounded and
unlabeled data streams. However, one limitation of most of the existing
approaches is represented by their limited ability to handle multivariate and
high-dimensional data, which is frequently observed in modern applications such
as traffic flow prediction, human activity recognition, and smart grids
monitoring. In this paper, we attempt to fill this gap by proposing WATCH, a
novel Wasserstein distance-based change point detection approach that models an
initial distribution and monitors its behavior while processing new data
points, providing accurate and robust detection of change points in dynamic
high-dimensional data. An extensive experimental evaluation involving a large
number of benchmark datasets shows that WATCH is capable of accurately
identifying change points and outperforming state-of-the-art methods.
