How Eective are Neural Networks at Forecasting and Prediction? A Review and Evaluation

Despite increasing applications of arti®cial neural networks (NNs) to forecasting over the past decade, opinions regarding their contribution are mixed. Evaluating research in this area has been dicult, due to lack of clear criteria. We identi®ed eleven guidelines that could be used in evaluating this literature. Using these, we examined applications of NNs to business forecasting and prediction. We located 48 studies done between 1988 and 1994. For each, we evaluated how e€ectively the proposed technique was compared with alternatives (e€ectiveness of validation) and how well the technique was implemented (e€ectiveness of implementation). We found that eleven of the studies were both e€ectively validated and implemented. Another eleven studies were e€ectively validated and produced positive results, even though there were some problems with respect to the quality of their NN implementations. Of these 22 studies, 18 supported the potential of NNs for forecasting and prediction. # 1998 John Wiley & Sons, Ltd.