WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data
This addresses a bottleneck in unsupervised change point detection for applications like traffic flow prediction and smart grids monitoring, though it is incremental as it builds on existing Wasserstein distance concepts.
The paper tackles the problem of detecting change points in high-dimensional time series data, which existing methods struggle with, and proposes WATCH, a Wasserstein distance-based approach that outperforms state-of-the-art methods in experiments on benchmark datasets.
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.