Ingeniería e Investigación
versão impressa ISSN 0120-5609
Artificial neural networks, especially multilayer perceptrons, have been recognised as being a powerful technique for forecasting nonlinear time series; however, cascade-correlation architecture is a strong competitor in this task due to it incorporating several advantages related to the statistical identification of multilayer perceptrons. This paper compares the accuracy of a cascadecorrelation neural network to the linear approach, multilayer perceptrons and dynamic architecture for artificial neural networks (DAN2) to determine whether the cascade-correlation network was able to forecast the time series being studied with more accuracy. It was concluded that cascade-correlation was able to forecast time series with more accuracy than other approaches.
Palavras-chave : cascade correlation; neural network; time series; forecasting; fit; validation; multilayer perceptron; DAN2; Arima.