A Contrastive Approach to Online Change Point Detection
This work addresses the need for flexible and efficient change point detection in real-time data streams, though it appears incremental as it builds on existing discrepancy maximization ideas.
The authors tackled the problem of online change point detection by proposing a novel procedure that maximizes discrepancy between pre-change and post-change distributions, achieving non-asymptotic bounds on average running length and expected detection delay, with efficiency demonstrated on synthetic and real-world data.
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 a flexible procedure 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.