Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7899
Title: Nonlinear model selection for PARMA processes using RJMCMC
Authors: Karakuş, Oktay
Kuruoğlu, Ercan Engin
Altınkaya, Mustafa Aziz
Issue Date: 2017
Publisher: IEEE
Abstract: Many prediction studies using real life measure-ments such as wind speed, power, electricity load and rain-fall 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. © EURASIP 2017.
Description: 25th European Signal Processing Conference, EUSIPCO 2017 -- 28 August 2017 through 2 September 2017
URI: https://doi.org/10.23919/EUSIPCO.2017.8081571
https://hdl.handle.net/11147/7899
ISBN: 9780992862671
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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