Document - Nonlinear model selection for PARMA processes using RJMCMC

2017

Conference article Open Access

Nonlinear model selection for PARMA processes using RJMCMC

Karakuå? O., Kuruoglu E. E., Altinkaya M. A.

Bayesian estimation Model selection Polynomial ARMA processes Reversible jump Markov chain Monte Carlo

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.

Source: EUSIPCO 25th European Signal Processing Conference, Kos, Greece, 28 August - 2 September 2017

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@inproceedings{oai:it.cnr:prodotti:376412,
	title = {Nonlinear model selection for PARMA processes using RJMCMC},
	author = {Karakuå? O. and Kuruoglu E.  E. and Altinkaya M.  A.},
	doi = {10.23919/eusipco.2017.8081571 and 10.5281/zenodo.1159433 and 10.5281/zenodo.1159434},
	booktitle = {EUSIPCO 25th European Signal Processing Conference, Kos, Greece, 28 August - 2 September 2017},
	year = {2017}
}