PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Karakuş O., Kuruoglu E. E., Altinkaya M. A. Nonlinear model selection for PARMA processes using RJMCMC. In: EUSIPCO - 25th European Signal Processing Conference (Kos, Greece, 28 August - 2 September 2017). Proceedings, pp. 2110 - 2114. EURASIP, 2017.
 
 
Abstract
(English)
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.
Subject Model selection
Reversible jump Markov chain Monte Carlo
Bayesian estimation
Polynomial ARMA processes
G.3 PROBABILITY AND STATISTICS - Probabilistic algorithms (including Monte Carlo)
I.2.6 ARTIFICIAL INTELLIGENCE. Learning. Parameter Learning
62F15 Bayesian inference
80M31 Monte Carlo methods
68Q32 Computational learning theory


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