A Primer On Neural Networks For Forecasting

A PRIMER ON NEURAL NETWORKS FOR FORECASTING By J. Stuart McMenamin In most forecasting problems the optimal number of nodes appears to be between two and five ... in econometric model selection, some qualitative criteria such as signs of parameters and absolute magnitudes of elasticities may be used, whereas in neural networks, it is usually based on the best fit ... neural networks provide a flexible nonlinear modeling framework that can have significant advantages. Artificial neural network models are beginning to be used in the electric utility industry for short-term forecasting. The neural network framework provides a flexible function that can approximate a wide range of nonlinear processes. In forecasting problems where nonlinearities and variable interactions are important, neural networks can provide significant advantages. Despite these advantages, the topic of neural networks is surrounded by confusion and controversy. In part, this reflects the fact that a different language is used for neural networks than is used in the more familiar (to forecasters) area of econometrics. The main purpose of this article is to bridge this language gap. The discussion ...