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Time series forecasting using cascade correlation networks
Predicción de series de tiempo con redes cascada-correlación
DOI:
https://doi.org/10.15446/ing.investig.v30n1.15226Keywords:
cascade correlation, neural network, time series, forecasting, fit, validation, multilayer perceptron, DAN2, Arima (en)cascada-correlación, redes neuronales, series de tiempo, predicción, entrenamiento, validación, perceptrón multicapa, DANN2, Arima (es)
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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-correlation 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.
Las redes neuronales, y en particular los perceptrones multicapa (MLP), han sido reconocidos como una de las más poderosas técnicas para estimar series de tiempo; sin embargo, la técnica de redes cascada-correlación (CC) es un fuerte competidor para pronosticar series temporales pues incorpora mejoras a los problemas de identificabilidad estadística del modelo del MLP. En éste artículo se compara el rendimiento de las redes CC respecto de otras técnicas, entre ellas el MLP, ANN y Arima, usando varias series de tiempo no lineales del mundo real, con el fin de determinar si las CC ofrecen buenos resultados en la práctica. Los resultados indican que las redes CC, en la mayoría de los casos, son superiores a los MLP, ANN y Arima, logrando errores menores en magnitud que los reportados en la literatura usando dichas técnicas, mientras que en relación a DAN2 se lograron errores cercanos e incluso mejores.
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Copyright (c) 2010 Juan David Velásquez, Fernán Alonso Villa, Reinaldo C. Souza
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