Integrating generalized linear auto-regression and artificial neural networks for coal demand forecasting

Abstract

In this study, a novel combined forecasting model integrating generalized linear auto-regression (GLAR) with artificial neural networks (ANN) was proposed to obtain accurate prediction results and ameliorate forecasting performances. We compare the performance of the new model with the two individual forecasting models-GLAR and ANN. Empirical results obtained reveal that the prediction using the combined model is generally better than those obtained using the individual model of GLAR and ANN in terms of the same evaluation measurements. Our findings reveal that the combined model proposed here can be used as an alternative forecasting tool for coal demand to achieve greater forecasting accuracy and improve prediction quality further. © 2009 Springer Berlin Heidelberg.