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Title: Non near model selection for PARMA processes using RJMCMC
Authors: Karakuş, Oktay
Kuruoğlu, Ercan E.
Altınkaya, Mustafa Aziz
Karakuş, Oktay
Altınkaya, Mustafa Aziz
Izmir Institute of Technology. Electronics and Communication Engineering
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Series/Report no.: European Signal Processing Conference
Abstract: Many prediction studies using real life measurements such as wind speed, power, electricity load and rainfall utilize linear autoregressive moving average (ARMA) based models due to their simplicity and general character. However, most of the real life applications exhibit nonlinear character and modelling them with linear time series may become problematic. Among nonlinear ARMA models, polynomial ARMA (PARMA) models belong to the class of linear-in-the-parameters. In this paper, we propose a reversible jump Markov chain Monte Carlo (RJMCMC) based complete model estimation method which estimates PARMA models with all their parameters including the nonlinearity degree. The proposed method is unique in the manner of estimating the nonlinearity degree and all other model orders and model coefficients at the same time. Moreover, in this paper, RJMCMC has been examined in an anomalous way by performing transitions between linear and nonlinear model spaces.
Description: 25th European Signal Processing Conference (EUSIPCO) -- AUG 28-SEP 02, 2017 -- GREECE
Karakus, Oktay/0000-0001-8009-9319; Altinkaya, Mustafa A/0000-0001-8048-5850
WOS: 000426986000415
ISBN: 978-0-9928-6267-1
ISSN: 2076-1465
Appears in Collections:WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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