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arXiv · 2408.14073

Score-based change point detection via tracking the best of infinitely many experts

Anna Markovich·Nikita Puchkin
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Abstract

We propose an algorithm for nonparametric online change point detection based on sequential score function estimation and the tracking the best expert approach. The core of the procedure is a version of the fixed share forecaster tailored to the case of infinite number of experts and quadratic loss functions. The algorithm shows promising results in numerical experiments on artificial and real-world data sets. Its performance is supported by rigorous high-probability bounds describing behaviour of the test statistic in the pre-change and post-change regimes.

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Anna Markovich, Nikita Puchkin. 2024-08-26. Score-based change point detection via tracking the best of infinitely many experts. https://arxiv.org/abs/2408.14073

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