Neural network optimization tool based on predictive MDL principle for time series prediction

An optimization tool for neural network architecture selection is presented. The main aim of the optimization tool is to reduce the size and complexity of the network and use the least number of weights and nodes for modeling and predictions on nonlinear time series. The problem of selecting the number of input and hidden nodes for modeling nodes is studied by the predictive minimum description length (MDL) principle. The authors discuss comparatively the performance of neural networks and conventional methods in predicting nonlinear time series. The neural network is found to yield better predictions than an optimum ARMA (autoregressive moving average) model