Detecting change points in time series, i.e., points in time at which some observed process suddenly changes, is a fundamental task that arises in many real-world applications, with consequences for safety and reliability. In this work, we propose ADAGA, a novel Gaussian process-based solution to this problem, that leverages a powerful heuristics we developed based on statistical hypothesis testing. In contrast to prior approaches, ADAGA adapts to changes both in mean and covariance structure of the temporal process. In extensive experiments, we show its versatility and applicability to different classes of change points, demonstrating that it is significantly more accurate than current state-of-the-art alternatives.

@inproceedings{caldarelli22adaptive,
	author = {Edoardo Caldarelli and Philippe Wenk and Stefan Bauer and Andreas Krause},
	booktitle = {Proc. International Conference for Machine Learning (ICML)},
	month = {July},
	pdf = {https://proceedings.mlr.press/v162/caldarelli22a/caldarelli22a.pdf},
	title = {Adaptive {G}aussian Process Change Point Detection},
	year = {2022}}