Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous surge in research activities in the past decade. While ANNs provide a great deal of promise, they also embody much uncertainty. Researchers to date are still not certain about the effect of key factors on forecasting performance of ANNs. This paper presents a state-of-the-art survey of ANN applications in forecasting. | ELSEVIER International Journal of Forecasting 14 1998 35-62 Forecasting with artificial neural networks The state of the art Guoqiang Zhang B. Eddy Patuwo Michael Y. Hu Graduate School of Management Kent State University Kent Ohio 44242-0001 USA Accepted 31 July 1997 Abstract Interest in using artificial neural networks ANNs for forecasting has led to a tremendous surge in research activities in the past decade. While ANNs provide a great deal of promise they also embody much uncertainty. Researchers to date are still not certain about the effect of key factors on forecasting performance of ANNs. This paper presents a state-of-the-art survey of ANN applications in forecasting. Our purpose is to provide 1 a synthesis of published research in this area 2 insights on ANN modeling issues and 3 the future research directions. 1998 Elsevier Science . Keywords Neural networks Forecasting 1. Introduction Recent research activities in artificial neural networks ANNs have shown that ANNs have powerful pattern classification and pattern recognition capabilities. Inspired by biological systems particularly by research into the human brain ANNs are able to learn from and generalize from experience. Currently ANNs are being used for a wide variety of tasks in many different fields of business industry and science Widrow et al. 1994 . One major application area of ANNs is forecasting Sharda 1994 . ANNs provide an attractive alternative tool for both forecasting researchers and practitioners. Several distinguishing features of ANNs make them valuable and attractive for a Corresponding author. Tel. 11 330 6722772 ext. 326 fax 11 330 6722448 e-mail mhu@ forecasting task. First as opposed to the traditional model-based methods ANNs are data-driven self-adaptive methods in that there are few a priori assumptions about the models for problems under study. They learn from examples and capture subtle functional relationships among the data even if the underlying .
