Are neural networks able to forecast nonlinear time series with moving average components?

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dc.citation.journalTitleIeee Latin America Transactionseng
dc.contributor.authorCogollo, M.R.
dc.contributor.authorVelásquez, J.D.
dc.contributor.departmentUniversidad EAFIT. Escuela de Cienciasspa
dc.contributor.researchgroupModelado Matemáticospa
dc.date.accessioned2021-04-12T14:07:10Z
dc.date.available2021-04-12T14:07:10Z
dc.date.issued2015-07-01
dc.description.abstractIn nonlinear time series forecasting, neural networks are interpreted as a nonlinear autoregressive models because they take as inputs the previous values of the time series. However, the use of neural networks to forecast nonlinear time series with moving components is an issue usually omitted in the literature. In this article, we investigate the use of traditional neural networks for forecasting nonlinear time series with moving average components and we demonstrate the necessity of formulating new neural networks to adequately forecast this class of time series. Experimentally we show that traditional neural networks are not able to capture all the behavior of nonlinear time series with moving average components, which leads them to have a low capacity of forecast. © 2015 IEEE.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1758
dc.identifier.doi10.1109/TLA.2015.7273790
dc.identifier.issn15480992
dc.identifier.otherWOS;000362037700036
dc.identifier.otherSCOPUS;2-s2.0-84942877476
dc.identifier.urihttp://hdl.handle.net/10784/27761
dc.language.isospaeng
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84942877476&doi=10.1109%2fTLA.2015.7273790&partnerID=40&md5=651d4138646615a07d04f255d250bd09
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1548-0992
dc.sourceIeee Latin America Transactions
dc.subject.keywordForecastingeng
dc.subject.keywordNeural networkseng
dc.subject.keywordMoving averageseng
dc.subject.keywordMoving componentseng
dc.subject.keywordNonlinear autoregressive modeleng
dc.subject.keywordNonlinear time serieseng
dc.subject.keywordTime serieseng
dc.titleAre neural networks able to forecast nonlinear time series with moving average components?eng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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