Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation
Hui Chen, Yinxu Jia, Guanghui Wang, Changliang Zou
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.
BibTeX
@article{Chen_Jia_Wang_Zou_2024, title={Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29008}, DOI={10.1609/aaai.v38i10.29008}, abstractNote={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.}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Hui and Jia, Yinxu and Wang, Guanghui and Zou, Changliang}, year={2024}, month={Mar.}, pages={11294-11301} }