%0 Journal Article
%T Time series forecasting using cascade correlation networks
%A Fern¨¢n Alonso Villa
%A Juan David Vel¨¢squez
%A Reinaldo C. Souza
%J -
%D 2010
%X 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 cascade-co- rrelation 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 accu- racy. It was concluded that cascade-correlation was able to forecast time series with more accuracy than other approaches
%K cascade correlation
%K neural network
%K time series
%K forecasting
%K fit
%K validation
%K multilayer perceptron
%K DAN2
%K Arima
%U https://revistas.unal.edu.co/index.php/ingeinv/article/view/15226