Adversarial density ratio estimation for change point detection

Change Point Detection (CPD) models are used to identify abrupt changes in the distribution of a data stream and have a widespread practical use. CPD methods generally compare the distribution of data sequences before and after a given time step to infer if there is a shift in distribution at the said time step. Numerous divergence measures, which measure distance between data distributions of sequence pairs, have been proposed for CPD [17, 20] and often the choice of divergence measure depends on the data used. Density Ratio Estimation (DRE) [18, 20] can be used to estimate a broad fam-ily of 𝑓 -divergences, which includes widely used CPD divergences like Kullback-Leibler (KL) and Pearson, and thus DRE is a popular approach for CPD. In this work, we improve upon the existing DRE techniques for CPD, by proposing a novel objective that combines DRE seamlessly with adversarial sample generation. The adversarial samples allows for a robust CPD with DRE to track subtle changes in distribution, leading to a reduction in false negatives. We experiment on a wide variety of real-world, public benchmark datasets to show that our approach improves upon existing state-of-the-art (SoTA) methods, including DRE based CPD methods, by demonstrating an ∼ 5% increase in 𝐹 −score