Noise-contrastive Online Change Point Detection

Abstract

We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to flexible algorithms suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets.

The preliminary version of this paper was presented at the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023, PMLR 206:5686-5713)