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arXiv · 2312.11319

Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation

Hui Chen·Yinxu Jia·Guanghui Wang·Changliang Zou
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Abstract

Accurately detecting multiple change-points is critical for various applications, but determining the optimal number of change-points remains a challenge. Existing approaches based on information criteria attempt to balance goodness-of-fit and model complexity, but their performance varies depending on the model. Recently, data-driven selection criteria based on cross-validation has been proposed, but these methods can be prone to slight overfitting in finite samples. In this paper, we introduce a method that controls the probability of overestimation and provides uncertainty quantification for learning multiple change-points via cross-validation. We frame this problem as a sequence of model comparison problems and leverage high-dimensional inferential procedures. We demonstrate the effectiveness of our approach through experiments on finite-sample data, showing superior uncertainty quantification for overestimation compared to existing methods. Our approach has broad applicability and can be used in diverse change-point models.

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Hui Chen, Yinxu Jia, Guanghui Wang, Changliang Zou. 2023-12-18. Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation. https://arxiv.org/abs/2312.11319

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