Offline Changepoint Detection With Gaussian Processes

Janneke Verbeek, Tom Heskes, Yuliya Shapovalova

Keywords: Gaussian processes, change point detection

TL;DR: An offline approach for change point detection based on Gaussian processes which does not require a priori knowledge of types of changes

Abstract: This work proposes Segmenting changepoint Gaussian process regression (SegCPGP), an offline changepoint detection method that integrates Gaussian process regression with the changepoint kernel, the likelihood ratio test and binary search. We use the spectral mixture kernel to detect various types of changes without prior knowledge of their type. SegCPGP outperforms state-of-the-art methods when detecting various change types in synthetic datasets; in real world changepoint detection datasets, it performs on par with its competitors. While its hypothesis test shows slight miscalibration, we find SegCPGP remains reasonably reliable.

Code Link: https://github.com/JVerbeek/segcpgp/

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission801/Authors, auai.org/UAI/2025/Conference/Submission801/Reproducibility_Reviewers

Submission Number: 801