Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks
| dc.citation.epage | 189 | |
| dc.citation.issue | 15 | |
| dc.citation.journalAbbreviatedTitle | ing.cienc. | eng |
| dc.citation.journalTitle | Ingeniería y Ciencia | eng |
| dc.citation.spage | 171 | |
| dc.citation.volume | 8 | |
| dc.contributor.affiliation | Universidad Nacional de Colombia | spa |
| dc.contributor.author | Velásquez, J D | spa |
| dc.contributor.author | Franco, C J | spa |
| dc.coverage.spatial | 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 | eng |
| dc.date | 2012-06-15 | |
| dc.date.accessioned | 2019-11-22T18:48:59Z | |
| dc.date.available | 2019-11-22T18:48:59Z | |
| dc.date.issued | 2012-06-15 | |
| dc.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. | eng |
| dc.description | 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. | spa |
| dc.format | application/pdf | |
| dc.identifier.doi | 10.17230/ingciencia.8.15.9 | |
| dc.identifier.issn | 2256-4314 | |
| dc.identifier.issn | 1794-9165 | |
| dc.identifier.uri | http://hdl.handle.net/10784/14444 | |
| dc.language.iso | eng | eng |
| dc.publisher | Universidad EAFIT | spa |
| dc.relation.isversionof | http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/943 | |
| dc.relation.uri | http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/943 | |
| dc.rights | Copyright (c) 2012 J D Velásquez, C J Franco | eng |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | eng |
| dc.rights.local | Acceso abierto | spa |
| dc.source | instname:Universidad EAFIT | |
| dc.source | reponame:Repositorio Institucional Universidad EAFIT | |
| dc.source | Ingeniería y Ciencia; Vol 8, No 15 (2012) | spa |
| dc.subject.keyword | Prediction | eng |
| dc.subject.keyword | Nonlinear Models | eng |
| dc.subject.keyword | Sarima | eng |
| dc.subject.keyword | Multilayer Perceptron | eng |
| dc.subject.keyword | Predicción | spa |
| dc.subject.keyword | Modelos No Lineales | spa |
| dc.subject.keyword | Sarima | spa |
| dc.subject.keyword | Perceptrón Multicapa | spa |
| dc.title | Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks | eng |
| dc.title | Pronóstico de series de tiempo con tendencia y ciclo estacional usando el modelo airline y redes neuronales artificiales | spa |
| dc.type | article | eng |
| dc.type | info:eu-repo/semantics/article | eng |
| dc.type | publishedVersion | eng |
| dc.type | info:eu-repo/semantics/publishedVersion | eng |
| dc.type.local | Artículo | spa |