%0 Journal Article
%T Predicci¨®n de series de tiempo con redes cascada-correlaci¨®n
%A Vel¨¢squez
%A Juan David
%A Villa
%A Fern¨¢n Alonso
%A Souza
%A Reinaldo C
%J Ingenier¨ªa e Investigaci¨®n
%D 2010
%I Universidad Nacional de Colombia
%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 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.
%K cascade correlation
%K neural network
%K time series
%K forecasting
%K fit
%K validation
%K multilayer perceptron
%K dan2
%K arima.
%U http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S0120-56092010000100027&lng=en&nrm=iso&tlng=en