A NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF THE BOX-JENKINS MODEL

Abstract

This study presents an artificial neural network-based paradigm for automating the controversial identification stage of the Box-Jenkins method, in which a time series is classified into an autoregressive moving average (ARMA) model. The identification stage depends on interpreting the patterns of two statistics-the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The interpretation, however, requires an expertise of the Box-Jenkins method to be successfully completed. This operational drawback makes it less practical despite its theoretical elaborateness. In this paper, a neural network approach is used to extract enough useful information from the patterns of ACF and PACF to identify an appropriate ARMA model for an unknown time series. This study suggests both the neural network architecture and the training strategy that are suitable for identifying the Box-Jenkins model. Promising results were obtained through extensive computer experiments with the artificially generated time series and managerial and economic data found in the real world.