Comparison of neural networks to statistical techniques for prediction of time series generated by nonlinear dynamic systems

The following paper is focused on comparison of neural networks to statistical techniques for time series prediction. Four statistical models, the ARIMA, the exponential smoothing, the exponential growth and the bilinear model are compared to two neural network architectures, the hierarchical multilayer perceptron and the ontogenic cascade correlation network. The intercomparison was done on two examples, a generic and a real-world one. The results of analyses were most promising from the neural networks point of view