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

Liyan Xie; Shaofeng Zou; Yao Xie; Venugopal V. Veeravalli

doi:10.1109/JSAIT.2021.3072962

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

Liyan Xie
, Shaofeng Zou
, Yao Xie
, Venugopal V. Veeravalli

Department of Computer Science and Engineering

University at Buffalo

Georgia Institute of Technology
University of Illinois at Urbana-Champaign

Research output: Contribution to journal › Article › peer-review

135 Scopus citations

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.

Original language	English
Article number	9403387
Pages (from-to)	494-514
Number of pages	21
Journal	IEEE Journal on Selected Areas in Information Theory
Volume	2
Issue number	2
DOIs	https://doi.org/10.1109/JSAIT.2021.3072962
State	Published - Jun 2021

Keywords

Sequential analysis
change point detection
network applications
sequential detection
time series

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10.1109/JSAIT.2021.3072962

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KW - time series

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

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Keywords

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