Design of experiments on neural network's training for nonlinear time series forecasting

In this study, the statistical methodology of Design of Experiments (DOE) was applied to better determine the parameters of an Artificial Neural Network (ANN) in a problem of nonlinear time series forecasting. Instead of the most common trial and error technique for the ANN's training, DOE was found to be a better methodology. The main motivation for this study was to forecast seasonal nonlinear time series-that is related to many real problems such as short-term electricity loads, daily prices and returns, water consumption, etc. A case study adopting this framework is presented for six time series representing the electricity load for industrial consumers of a production company in Brazil. (C) 2008 Elsevier B.V. All rights reserved.