Decision support for ARMA model identification using hierarchically organized neural networks
Won Chul Jhee, H.B. Ro · 2002
To resolve the difficulties in autoregressive moving average (ARMA) model identification, the extended sample autocorrelation function (ESACF) is adopted as a feature extractor, and the multilayered backpropagation network (MLBPN) is used as a pattern classifier. To improve the classification power of MLBPNs, a hierarchically organized neural network is proposed, which consists of an AR network and many small-sized MA networks. The output of the AR network determines the AR order of a time series, and designates the MA network which will give the MA order. A step-by-step training strategy is also suggested so that the learned MPBPNs can effectively classify ESACF patterns contaminated by a high level of noise. The experiment with the artificially generated test data and real world data showed promising results.>