Sequential (Quickest) Change Detection: Classical Results and New Directions

TL;DR

This paper reviews classical and modern approaches to sequential change detection, highlighting new directions, theoretical extensions, and applications across various fields.

Contribution

It provides a comprehensive overview of foundational methods and explores recent advancements and interdisciplinary extensions in sequential change detection.

Findings

Summarizes classical results in change detection theory.

Discusses recent extensions and new research directions.

Highlights applications in signal processing and statistics.

Abstract

Online detection of changes in stochastic systems, referred to as sequential change detection or quickest change detection, is an important research topic in statistics, signal processing, and information theory, and has a wide range of applications. This survey starts with the basics of sequential change detection, and then moves on to generalizations and extensions of sequential change detection theory and methods. We also discuss some new dimensions that emerge at the intersection of sequential change detection with other areas, along with a selection of modern applications and remarks on open questions.