DocumentCode :
730641
Title :
Particle filtering of ARMA processes of unknown order and parameters
Author :
Urteaga, Inigo ; Djuric, Petar M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Stony Brook Univ., Stony Brook, NY, USA
fYear :
2015
fDate :
19-24 April 2015
Firstpage :
4105
Lastpage :
4109
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.
Keywords :
particle filtering (numerical methods); time series; ARMA time-series; Rao-Blackwellization; hidden ARMA state processes; innovation parameters; nonlinear functions; particle filtering method; state-space models; Computational modeling; Covariance matrices; Estimation; Mathematical model; Noise; State-space methods; Technological innovation; ARMA models; Rao-Blackwellization; State-space models; particle filtering; time-series;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
Type :
conf
DOI :
10.1109/ICASSP.2015.7178743
Filename :
7178743
Link To Document :
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