Skip to main navigation Skip to search Skip to main content

Particle filtering of ARMA processes of unknown order and parameters

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper considers inference on the widely used state-space models described by hidden ARMA state processes of unknown order observed via non-linear functions of the states. We propose a particle filtering method for sequentially inferring the unknown ARMA time-series by Rao-Blackwellization of all the static unknowns. Our method does not rely either on any assumption on the model order or on the static ARMA and state innovation parameters. Consequently, when the ARMA model order is unknown, it can be used without a follow-up model selection procedure. Extensive simulation results validate the proposed method across different ARMA models.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4105-4109
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - Aug 4 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: Apr 19 2014Apr 24 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Conference

Conference40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Country/TerritoryAustralia
CityBrisbane
Period04/19/1404/24/14

Keywords

  • ARMA models
  • particle filtering
  • Rao-Blackwellization
  • State-space models
  • time-series

Fingerprint

Dive into the research topics of 'Particle filtering of ARMA processes of unknown order and parameters'. Together they form a unique fingerprint.

Cite this