Hierarchical Bayesian of ARMA Models Using Simulated Annealing Algorithm
Abstract: When the
Autoregressive Moving Average (ARMA) model is fitted with real data, the actual
value of the model order and the model parameter are often unknown. The goal of
this paper is to find an estimator for the model order and the model parameter
based on the data. In this paper, the model order identification and model
parameter estimation is given in a hierarchical Bayesian framework. In this
framework, the model order and model parameter are assumed to have prior
distribution, which summarizes all the information available about the process.
All the information about the characteristics of the model order and the model
parameter are expressed in the posterior distribution. Probability
determination of the model order and the model parameter requires the
integration of the posterior distribution resulting. It is an operation which
is very difficult to be solved analytically. Here the Simuated Annealing
Reversible Jump Markov Chain Monte Carlo (MCMC) algorithm was developed to
compute the required integration over the posterior distribution simulation.
Methods developed are evaluated in simulation studies in a number of set of
synthetic data and real data.
Author: S. Suparman, Michel
Doisy
Journal Code: jptkomputergg140029
