A comparison of exponential smoothing and neural networks in time series prediction

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...

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Autores:
Velásquez Henao, Juan David
Zambrano Pérez, Cristian Olmedo
Franco Cardona, Carlos Jaime
Tipo de recurso:
Article of journal
Fecha de publicación:
2013
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/74156
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/74156
http://bdigital.unal.edu.co/38633/
Palabra clave:
Forecasts combination
nonlinear models
artifi cial neural networks
nonlinear time series
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Velásquez Henao, Juan David6e2af894-3c59-45bb-95d4-43331c36572d300Zambrano Pérez, Cristian Olmedo07c1c66b-27f8-4947-b35f-2cc8dc9e0728300Franco Cardona, Carlos Jaime070ee56f-5115-438d-ab21-4368f0109d593002019-07-03T17:23:45Z2019-07-03T17:23:45Z2013https://repositorio.unal.edu.co/handle/unal/74156http://bdigital.unal.edu.co/38633/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.application/pdfspaUniversidad Nacional de Colombia Sede Medellínhttp://revistas.unal.edu.co/index.php/dyna/article/view/41564Universidad Nacional de Colombia Revistas electrónicas UN DynaDynaDYNA; Vol. 80, núm. 182 (2013); 66-73 Dyna; Vol. 80, núm. 182 (2013); 66-73 2346-2183 0012-7353Velásquez Henao, Juan David and Zambrano Pérez, Cristian Olmedo and Franco Cardona, Carlos Jaime (2013) A comparison of exponential smoothing and neural networks in time series prediction. DYNA; Vol. 80, núm. 182 (2013); 66-73 Dyna; Vol. 80, núm. 182 (2013); 66-73 2346-2183 0012-7353 .A comparison of exponential smoothing and neural networks in time series predictionArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTForecasts combinationnonlinear modelsartifi cial neural networksnonlinear time seriesORIGINAL41564-188428-1-PB.pdfapplication/pdf551555https://repositorio.unal.edu.co/bitstream/unal/74156/1/41564-188428-1-PB.pdf638b18fce7b948fbfcc8f3625d154708MD51THUMBNAIL41564-188428-1-PB.pdf.jpg41564-188428-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg9802https://repositorio.unal.edu.co/bitstream/unal/74156/2/41564-188428-1-PB.pdf.jpge1f445117536fb64cb9714dd11c3bb79MD52unal/74156oai:repositorio.unal.edu.co:unal/741562023-07-02 23:03:37.552Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv A comparison of exponential smoothing and neural networks in time series prediction
title A comparison of exponential smoothing and neural networks in time series prediction
spellingShingle A comparison of exponential smoothing and neural networks in time series prediction
Forecasts combination
nonlinear models
artifi cial neural networks
nonlinear time series
title_short A comparison of exponential smoothing and neural networks in time series prediction
title_full A comparison of exponential smoothing and neural networks in time series prediction
title_fullStr A comparison of exponential smoothing and neural networks in time series prediction
title_full_unstemmed A comparison of exponential smoothing and neural networks in time series prediction
title_sort A comparison of exponential smoothing and neural networks in time series prediction
dc.creator.fl_str_mv Velásquez Henao, Juan David
Zambrano Pérez, Cristian Olmedo
Franco Cardona, Carlos Jaime
dc.contributor.author.spa.fl_str_mv Velásquez Henao, Juan David
Zambrano Pérez, Cristian Olmedo
Franco Cardona, Carlos Jaime
dc.subject.proposal.spa.fl_str_mv Forecasts combination
nonlinear models
artifi cial neural networks
nonlinear time series
topic Forecasts combination
nonlinear models
artifi cial neural networks
nonlinear time series
description 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.
publishDate 2013
dc.date.issued.spa.fl_str_mv 2013
dc.date.accessioned.spa.fl_str_mv 2019-07-03T17:23:45Z
dc.date.available.spa.fl_str_mv 2019-07-03T17:23:45Z
dc.type.spa.fl_str_mv Artículo de revista
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url https://repositorio.unal.edu.co/handle/unal/74156
http://bdigital.unal.edu.co/38633/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv http://revistas.unal.edu.co/index.php/dyna/article/view/41564
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Dyna
Dyna
dc.relation.ispartofseries.none.fl_str_mv DYNA; Vol. 80, núm. 182 (2013); 66-73 Dyna; Vol. 80, núm. 182 (2013); 66-73 2346-2183 0012-7353
dc.relation.references.spa.fl_str_mv Velásquez Henao, Juan David and Zambrano Pérez, Cristian Olmedo and Franco Cardona, Carlos Jaime (2013) A comparison of exponential smoothing and neural networks in time series prediction. DYNA; Vol. 80, núm. 182 (2013); 66-73 Dyna; Vol. 80, núm. 182 (2013); 66-73 2346-2183 0012-7353 .
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
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dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia Sede Medellín
institution Universidad Nacional de Colombia
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