ART: Distribution-Free and Model-Agnostic Changepoint Detection with Finite-Sample Guarantees

TL;DR

ART is a versatile, distribution-free changepoint detection framework that guarantees finite-sample error control and is applicable across various models and high-dimensional data.

Contribution

It introduces a novel, model-agnostic method with finite-sample guarantees for changepoint detection, extending to multi-scale and high-dimensional settings.

Findings

Provides exact Type-I error control in changepoint detection.

Extends to multi-scale settings for multiple changepoint estimation.

Applicable to high-dimensional data and machine learning contexts.

Abstract

We introduce ART, a distribution-free and model-agnostic framework for changepoint detection that provides finite-sample guarantees. ART transforms independent observations into real-valued scores via a symmetric function, ensuring exchangeability in the absence of changepoints. These scores are then ranked and aggregated to detect distributional changes. The resulting test offers exact Type-I error control, agnostic to specific distributional or model assumptions. Moreover, ART seamlessly extends to multi-scale settings, enabling robust multiple changepoint estimation and post-detection inference with finite-sample error rate control. By locally ranking the scores and performing aggregations across multiple prespecified intervals, ART identifies changepoint intervals and refines subsequent inference while maintaining its distribution-free and model-agnostic nature. This adaptability makes ART as a reliable and versatile tool for modern changepoint analysis, particularly in high-dimensional data contexts and applications leveraging machine learning methods.