A COMPARISON OF EXPONENTIAL SMOOTHING AND NEURAL NETWORKS IN TIME SERIES PREDICTION
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| Format: | Artículo científico |
| Language: | en |
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Universidad Nacional de Colombia
2013
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| author | JUAN DAVID VELÁSQUEZ HENAO |
| author_facet | JUAN DAVID VELÁSQUEZ HENAO |
| contents | A COMPARISON OF EXPONENTIAL SMOOTHING AND NEURAL NETWORKS IN TIME SERIES PREDICTION JUAN DAVID VELÁSQUEZ HENAO CRISTIAN OLMEDO ZAMBRANO PEREZ CARLOS JAIME FRANCO CARDONA Ingeniería nonlinear models Forecasts combination nonlinear time series artificial neural networks 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 fit but lower predictive power than the RBFNN; detrending and deseasonality allows the RBFNN to fit 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. 2013 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49629318009 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.182 Vol.80 |
| format | Artículo científico |
| id | redalyc_49629318009 |
| institution | Redalyc |
| language | en |
| publishDate | 2013 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | A COMPARISON OF EXPONENTIAL SMOOTHING AND NEURAL NETWORKS IN TIME SERIES PREDICTION JUAN DAVID VELÁSQUEZ HENAO Ingeniería nonlinear models Forecasts combination nonlinear time series artificial neural networks A COMPARISON OF EXPONENTIAL SMOOTHING AND NEURAL NETWORKS IN TIME SERIES PREDICTION JUAN DAVID VELÁSQUEZ HENAO CRISTIAN OLMEDO ZAMBRANO PEREZ CARLOS JAIME FRANCO CARDONA Ingeniería nonlinear models Forecasts combination nonlinear time series artificial neural networks 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 fit but lower predictive power than the RBFNN; detrending and deseasonality allows the RBFNN to fit 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. 2013 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49629318009 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.182 Vol.80 |
| title | A COMPARISON OF EXPONENTIAL SMOOTHING AND NEURAL NETWORKS IN TIME SERIES PREDICTION |
| topic | Ingeniería nonlinear models Forecasts combination nonlinear time series artificial neural networks |
| url | https://www.redalyc.org/articulo.oa?id=49629318009 |