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Nonlinear model selection for PARMA processes using RJMCMC

Karakus, O., Kuruoglu, E.E. and Altinkaya, M.A. 2017. Nonlinear model selection for PARMA processes using RJMCMC. Presented at: EUSIPCO 2017: 25th European Signal Processing Conference (EUSIPCO), 28 August-2 September 2017. 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, pp. 2056-2060. 10.23919/EUSIPCO.2017.8081571

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISBN: 9781538607510
Last Modified: 07 Feb 2022 11:14

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