ARMA model order determination and MDL: a new perspective

Much research has focused on the problem of estimating the model order of autoregressive moving average (ARMA) processes. The most well-known of the proposed solutions for this problem include the final prediction error (FPE), Akaike information criterion (AIC), and minimum description length (MDL). A new approach for model order determination based on the MDL criterion is proposed and shown to depend on the minimum eigenvalue of a covariance matrix derived from the observed data. As a result, a new selection procedure for estimating the model order via MDL is proposed. Examples that illustrate the significantly improved accuracy of the proposed technique are given