Noise-contrastive Online Change Point Detection

Nikita Puchkin; Artur Goldman; Konstantin Yakovlev; Valeriia Dzis; Uliana Vinogradova

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arXiv · 2206.10143

Noise-contrastive Online Change Point Detection

Nikita Puchkin·Artur Goldman·Konstantin Yakovlev·Valeriia Dzis·Uliana Vinogradova

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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.

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Nikita Puchkin, Artur Goldman, Konstantin Yakovlev, Valeriia Dzis, Uliana Vinogradova. 2022-06-21. Noise-contrastive Online Change Point Detection. https://arxiv.org/abs/2206.10143

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