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...
- 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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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 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.coarversion.spa.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
format |
http://purl.org/coar/resource_type/c_6501 |
status_str |
publishedVersion |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/74156 |
dc.identifier.eprints.spa.fl_str_mv |
http://bdigital.unal.edu.co/38633/ |
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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application/pdf |
dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia Sede Medellín |
institution |
Universidad Nacional de Colombia |
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