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

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

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Article number9403387
Pages (from-to)494-514
Number of pages21
JournalIEEE Journal on Selected Areas in Information Theory
Volume2
Issue number2
DOIs
StatePublished - Jun 2021

Keywords

  • change point detection
  • network applications
  • Sequential analysis
  • sequential detection
  • time series

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Media Technology
  • Artificial Intelligence
  • Applied Mathematics

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