High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability

2026-09-21

AI Summary

本文提出一种基于Sliced Wasserstein距离的在线变化点检测框架，通过自适应阈值调整和提高可解释性来解决高维数据中的计算挑战和实用性限制。

Abstract

Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial markets, cybersecurity, manufacturing, and autonomous systems. Traditional CPD methods often face computational challenges in high-dimensional settings and typically provide limited explanations for detected changes, which can restrict their practical usability. This paper introduces a CPD framework that improves scalability and interpretability by leveraging the Sliced Wasserstein (SW) distance. Our contributions are fourfold: (1) we transform multivariate sequential data into one-dimensional scores using the SW distance, making the resulting representation compatible with existing CPD methods; (2) we analyze the distributional behavior of random slices of the SW distance and show that, under suitable assumptions, they can be approximated by a Gamma distribution, providing a principled basis for threshold calibration; (3) we propose a self-adapting online CPD algorithm that combines this SW-based score with an adaptive quantile-based threshold; (4) we introduce a model-specific framework for generating contrastive explanations for annotated change points. Empirically, our method reduces false positives by at least $48\%$ on average compared with popular online and offline CPD baselines, while maintaining competitive or superior detection performance. Code is available at https://github.com/jsve96/SWCPD_Code. At the same time, it produces interpretable change-point annotations, making it practical for deployment in high-stakes applications.

Problem

• Change Point Detection
• High-Dimensional Data
• Interpretability
• Computational Challenges

Innovation

• Sliced Wasserstein Distance
• Adaptive Thresholding
• High-Dimensional Data
• Interpretability
• Change Point Detection