A simulation study of artificial neural networks for nonlinear time-series forecasting

This study presents an experimental evaluation of neural networks for nonlinear time-series forecasting. The effects of three main factors - input nodes, hidden nodes and sample size, are examined through a simulated computer experiment. Results show that neural networks are valuable tools for modeling and forecasting nonlinear time series while traditional linear methods are not as competent for this task. The number of input nodes is much more important than the number of hidden nodes in neural network model building for forecasting. Moreover, large sample is helpful to ease the overfitting problem.