Post Hoc Inference for Component Attribution in Multivariate Change-Point Detection

2026-07-16

AI Summary

This work addresses the challenge of accurately attributing detected change points in multivariate time series to specific subsets of variables. The authors propose a post-hoc, nonparametric testing framework that, after an offline change point has been identified, determines whether the change occurs in one of two pre-specified coordinate blocks or in both. Built upon two-sample nonparametric hypothesis testing, the method offers rigorous theoretical guarantees for Type I error control. Empirical evaluations on both synthetic and real-world datasets demonstrate that the proposed approach achieves high attribution accuracy and strong robustness in identifying the components responsible for the change.

Abstract

We consider the post-detection analysis of change-points for multivariate time series, with the goal of identifying which coordinates are responsible for a detected change. After a change-point has been located by an offline detection algorithm, we propose post hoc statistical procedures to determine whether the change occurs in either of two predefined blocks of coordinates or in both. Our methods rely on two-sample testing procedures with a particular focus on nonparametric tests; we provide theoretical guarantees for Type I error control. Simulations and a real-data experiment demonstrate the strong performance of the proposed procedures.

Problem

• change-point detection
• component attribution
• multivariate time series
• post hoc inference
• nonparametric testing

Innovation

• post hoc inference
• component attribution
• multivariate change-point detection
• nonparametric testing
• Type I error control