  This paper derives a Minimum Message Length (MML) criterion for the model
selection of the Autoregressive Moving Average (ARMA) time series model. The
MML87 performances on the ARMA model compared with other well known model
selection criteria, Akaike Information Criterion (AIC), Corrected AIC (AICc),
Bayesian Information Criterion (BIC), and Hannan Quinn (HQ). The experimental
results show that the MML87 is outperformed the other model selection criteria
as it select most of the models with lower prediction errors and the models
selected by MML87 to have a lower mean squared error in different in-sample and
out-sample sizes.
