Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
411141 | Neurocomputing | 2009 | 19 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
P.P. Balestrassi, E. Popova, A.P. Paiva, J.W. Marangon Lima,