This study was designed: (a) to investigate a simple neural-network solution to forecasting the special class of time series corresponding to a wide range of ARMA(p,q) structures; (b) to study the significance of matching the input window size with the nature of time series. The study adopted a simulation approach in conjunction with an experimental design. It is discovered that a simple two-layered network, with proper input window size, is able to consistently outperform the multi-layer feedforward network and that the two-layered network is comparable to the Box-Jenkins modelling approach for a majority of the ARMA(p,q) time series studied and better than the Box-Jenkins modelling approach when the ARMA structure gets more complex and generates more variability. The results affirm that it is unnecessary to use multi-layer feedforward networks for this special class of linear time series and that the two-layered network can be a useful forecasting alternative to the widely popular Box-Jenkins model.
