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

Authors:
P. P. Balestrassi;E. Popova;A. P. Paiva;J. W. Marangon Lima
Affiliations:
Federal University of Itajuba, Brazil;University of Texas at Austin, USA;Federal University of Itajuba, Brazil;Federal University of Itajuba, Brazil
Venue:
Neurocomputing
Year:
2009

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Abstract

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.