Predicción de series de tiempo con redes cascada-correlación

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.