Non-parametric prediction of AR processes using neural networks

A nonparametric neural network technique for prediction of future values of a signal based on its past history is presented. A multilayer feed-forward neural network is used. It develops an internal model of the signal through a training operation involving past history of the considered signal. Training is performed using the back-propagation algorithm. The trained net is then used to do the forecast. Training is continued during operation to improve performance. The net performance is tested on signals generated by autoregressive (AR) models of orders two to ten, and results are compared to optimal forecasts.<>