Neural Network Prediction of Non-Linear Time Series Using Predictive MDL Principle

A neural network approach for modelling and predictions on non-linear time series is presented. The main aim is to reduce the size and complexity of the network and use the least number of weights and nodes for any predictive mapping. The problem of selecting the number of input and hidden nodes is studied by the predictive minimum description length principle. We discuss comparatively the performance of neural networks and conventional methods in predicting non linear time series. The neural network is found to yield better predictions than an optimum ARMA model.