Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks

Many time series with trend and seasonal cycles are successfully modeled and predicted using the airline model of Box and Jenkins; However, the presence of nonlinearities in the data is neglected by this model. In this article, a new non-linear version of the airline model is proposed; for this, the...

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Autores:
Velásquez, J D
Franco, C J
Tipo de recurso:
Fecha de publicación:
2012
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/14444
Acceso en línea:
http://hdl.handle.net/10784/14444
Palabra clave:
Prediction
Nonlinear Models
Sarima
Multilayer Perceptron
Predicción
Modelos No Lineales
Sarima
Perceptrón Multicapa
Rights
License
Copyright (c) 2012 J D Velásquez, C J Franco
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spelling Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees2012-06-152019-11-22T18:48:59Z2012-06-152019-11-22T18:48:59Z2256-43141794-9165http://hdl.handle.net/10784/1444410.17230/ingciencia.8.15.9Many time series with trend and seasonal cycles are successfully modeled and predicted using the airline model of Box and Jenkins; However, the presence of nonlinearities in the data is neglected by this model. In this article, a new non-linear version of the airline model is proposed; for this, the linear component of moving averages is replaced by a multilayer perceptron. The proposed model is used to forecast two benchmark time series; It was found that the proposed model is capable of forecasting time series more accurately than other traditional approaches.Muchas series de tiempo con tendencia y ciclos estacionales son exitosamente modeladas y pronosticadas usando el modelo airline de Box y Jenkins; sin embargo, la presencia de no linealidades en los datos son despreciadas por este modelo. En este artículo, se propone una nueva versión no lineal del modelo airline; para esto, se reemplaza la componente lineal de promedios móviles por un perceptrón multicapa. El modelo propuesto es usado para pronosticar dos series de tiempo benchmark; se encontró que el modelo propuesto es capaz de pronosticar las series de tiempo con mayor precisión que otras aproximaciones tradicionales.application/pdfengUniversidad EAFIThttp://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/943http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/943Copyright (c) 2012 J D Velásquez, C J FrancoAcceso abiertohttp://purl.org/coar/access_right/c_abf2instname:Universidad EAFITreponame:Repositorio Institucional Universidad EAFITIngeniería y Ciencia; Vol 8, No 15 (2012)Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networksPronóstico de series de tiempo con tendencia y ciclo estacional usando el modelo airline y redes neuronales artificialesarticleinfo:eu-repo/semantics/articlepublishedVersioninfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1PredictionNonlinear ModelsSarimaMultilayer PerceptronPredicciónModelos No LinealesSarimaPerceptrón MulticapaVelásquez, J D4bebabc0-254d-4f2d-bac8-3aa4648a9cc3-1Franco, C Jee8d02a9-9241-426a-a9f2-ee423b8e09f1-1Universidad Nacional de ColombiaIngeniería y Ciencia815171189ing.cienc.THUMBNAILminaitura-ig_Mesa de trabajo 1.jpgminaitura-ig_Mesa de trabajo 1.jpgimage/jpeg265796https://repository.eafit.edu.co/bitstreams/50cc0794-2690-4031-a8cd-b7465bd49ac0/downloadda9b21a5c7e00c7f1127cef8e97035e0MD52ORIGINAL9.pdf9.pdfTexto completo PDFapplication/pdf291962https://repository.eafit.edu.co/bitstreams/b7b7ca40-a4b1-4eef-8c5f-1139c5e0862d/downloadc0f4fa19e78293098cfbaf02f34195b3MD51articulo.htmlarticulo.htmlTexto completo HTMLtext/html373https://repository.eafit.edu.co/bitstreams/d2cd02f6-3a63-4222-8445-8e9f388e9de6/download5bd220ee9f54e7acbe7a0cb7a283f2ecMD5310784/14444oai:repository.eafit.edu.co:10784/144442024-12-04 11:48:53.834open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co
dc.title.eng.fl_str_mv Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks
dc.title.spa.fl_str_mv Pronóstico de series de tiempo con tendencia y ciclo estacional usando el modelo airline y redes neuronales artificiales
title Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks
spellingShingle Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks
Prediction
Nonlinear Models
Sarima
Multilayer Perceptron
Predicción
Modelos No Lineales
Sarima
Perceptrón Multicapa
title_short Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks
title_full Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks
title_fullStr Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks
title_full_unstemmed Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks
title_sort Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks
dc.creator.fl_str_mv Velásquez, J D
Franco, C J
dc.contributor.author.spa.fl_str_mv Velásquez, J D
Franco, C J
dc.contributor.affiliation.spa.fl_str_mv Universidad Nacional de Colombia
dc.subject.keyword.eng.fl_str_mv Prediction
Nonlinear Models
Sarima
Multilayer Perceptron
topic Prediction
Nonlinear Models
Sarima
Multilayer Perceptron
Predicción
Modelos No Lineales
Sarima
Perceptrón Multicapa
dc.subject.keyword.spa.fl_str_mv Predicción
Modelos No Lineales
Sarima
Perceptrón Multicapa
description Many time series with trend and seasonal cycles are successfully modeled and predicted using the airline model of Box and Jenkins; However, the presence of nonlinearities in the data is neglected by this model. In this article, a new non-linear version of the airline model is proposed; for this, the linear component of moving averages is replaced by a multilayer perceptron. The proposed model is used to forecast two benchmark time series; It was found that the proposed model is capable of forecasting time series more accurately than other traditional approaches.
publishDate 2012
dc.date.issued.none.fl_str_mv 2012-06-15
dc.date.available.none.fl_str_mv 2019-11-22T18:48:59Z
dc.date.accessioned.none.fl_str_mv 2019-11-22T18:48:59Z
dc.date.none.fl_str_mv 2012-06-15
dc.type.eng.fl_str_mv article
info:eu-repo/semantics/article
publishedVersion
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dc.identifier.issn.none.fl_str_mv 2256-4314
1794-9165
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10784/14444
dc.identifier.doi.none.fl_str_mv 10.17230/ingciencia.8.15.9
identifier_str_mv 2256-4314
1794-9165
10.17230/ingciencia.8.15.9
url http://hdl.handle.net/10784/14444
dc.language.iso.eng.fl_str_mv eng
language eng
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dc.relation.uri.none.fl_str_mv http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/943
dc.rights.eng.fl_str_mv Copyright (c) 2012 J D Velásquez, C J Franco
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Acceso abierto
rights_invalid_str_mv Copyright (c) 2012 J D Velásquez, C J Franco
Acceso abierto
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
dc.coverage.spatial.eng.fl_str_mv Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
dc.publisher.spa.fl_str_mv Universidad EAFIT
dc.source.none.fl_str_mv instname:Universidad EAFIT
reponame:Repositorio Institucional Universidad EAFIT
dc.source.spa.fl_str_mv Ingeniería y Ciencia; Vol 8, No 15 (2012)
instname_str Universidad EAFIT
institution Universidad EAFIT
reponame_str Repositorio Institucional Universidad EAFIT
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