A comparison of exponential smoothing and neural networks in time series prediction
Autores
Velásquez Henao, Juan David
Zambrano Pérez, Cristian Olmedo
Franco Cardona, Carlos Jaime
Director
Tipo de contenido
Artículo de revista
Idioma del documento
EspañolFecha de publicación
2013
Título de la revista
ISSN de la revista
Título del volumen
Documentos PDF
Resumen
In this article, we compare the accuracy of the forecasts for the exponential smoothing (ES) approach and the radial basis function neural networks (RBFNN) when three nonlinear time series with trend and seasonal cycle are forecasted. In addition, we consider the recommendations of preprocessing by eliminating the trend and seasonal cycle using simple and seasonal differentiation. Finally, we use forecast combining for determining if there is complementary information between the forecasts of the individual models. Our numerical evidence supports the following conclusions: ES models have a better fi t but lower predictive power than the RBFNN; detrending and deseasonality allows the RBFNN to fi t and forecast with more accuracy than the RBFNN trained with the original dataset; there is no evidence of information complementarity in the forecasts such that the methodology of forecasts combination is not able to predict with more accuracy than the RBFNN and ES methodologies.