State-of-the-Art in Sequential Change-Point Detection

TL;DR

This paper reviews the latest advances in sequential change-point detection across Bayesian, generalized Bayesian, and minimax frameworks, highlighting recent theoretical developments and practical case studies.

Contribution

It provides a comprehensive overview of current methods, connecting generalized Bayesian approaches with multi-cyclic detection, and includes illustrative case studies.

Findings

Latest algorithms for Bayesian and minimax detection

Connections between generalized Bayesian and multi-cyclic detection

Case studies demonstrating state-of-the-art applications

Abstract

We provide an overview of the state-of-the-art in the area of sequential change-point detection assuming discrete time and known pre- and post-change distributions. The overview spans over all major formulations of the underlying optimization problem, namely, Bayesian, generalized Bayesian, and minimax. We pay particular attention to the latest advances in each. Also, we link together the generalized Bayesian problem with multi-cyclic disorder detection in a stationary regime when the change occurs at a distant time horizon. We conclude with two case studies to illustrate the cutting edge of the field at work.