The appraisal of machine learning techniques for tourism demand forecasting

Machine learning (ML) methods are being increasingly used with forecasting purposes. This study assesses the predictive performance of several ML models in a multiple-input multiple-output (MIMO) setting that allows incorporating the cross-correlations between the inputs. We compare the forecast acc...

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Detalles Bibliográficos
Autores: Clavería González, Óscar, Monte Moreno, Enric, Torra Porras, Salvador
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/172665
Acceso en línea:https://hdl.handle.net/2445/172665
Access Level:acceso abierto
Palabra clave:Aprenentatge automàtic
Distribució de Gauss
Anàlisi de regressió
Xarxes neuronals convolucionals
Machine learning
Gaussian distribution
Regression analysis
Convolutional neural networks
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spelling The appraisal of machine learning techniques for tourism demand forecastingClavería González, ÓscarMonte Moreno, EnricTorra Porras, SalvadorAprenentatge automàticDistribució de GaussAnàlisi de regressióXarxes neuronals convolucionalsMachine learningGaussian distributionRegression analysisConvolutional neural networksMachine learning (ML) methods are being increasingly used with forecasting purposes. This study assesses the predictive performance of several ML models in a multiple-input multiple-output (MIMO) setting that allows incorporating the cross-correlations between the inputs. We compare the forecast accuracy of a Gaussian process regression (GPR) model to that of different neural network architectures in a multi-step-ahead time series prediction experiment. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for concatenation.Nova Science Publishers2020202020172020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion21 p.application/pdfhttps://hdl.handle.net/2445/172665Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://search.proquest.com/docview/2190325008?pq-origsite=gscholar&fromopenview=true#International Journal of Computer Research, 2017, vol. 24, num. 2/3, p. 173-193(c) Nova Science Publishers, 2017info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1726652026-05-29T05:05:01Z
dc.title.none.fl_str_mv The appraisal of machine learning techniques for tourism demand forecasting
title The appraisal of machine learning techniques for tourism demand forecasting
spellingShingle The appraisal of machine learning techniques for tourism demand forecasting
Clavería González, Óscar
Aprenentatge automàtic
Distribució de Gauss
Anàlisi de regressió
Xarxes neuronals convolucionals
Machine learning
Gaussian distribution
Regression analysis
Convolutional neural networks
title_short The appraisal of machine learning techniques for tourism demand forecasting
title_full The appraisal of machine learning techniques for tourism demand forecasting
title_fullStr The appraisal of machine learning techniques for tourism demand forecasting
title_full_unstemmed The appraisal of machine learning techniques for tourism demand forecasting
title_sort The appraisal of machine learning techniques for tourism demand forecasting
dc.creator.none.fl_str_mv Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
author Clavería González, Óscar
author_facet Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
author_role author
author2 Monte Moreno, Enric
Torra Porras, Salvador
author2_role author
author
dc.subject.none.fl_str_mv Aprenentatge automàtic
Distribució de Gauss
Anàlisi de regressió
Xarxes neuronals convolucionals
Machine learning
Gaussian distribution
Regression analysis
Convolutional neural networks
topic Aprenentatge automàtic
Distribució de Gauss
Anàlisi de regressió
Xarxes neuronals convolucionals
Machine learning
Gaussian distribution
Regression analysis
Convolutional neural networks
description Machine learning (ML) methods are being increasingly used with forecasting purposes. This study assesses the predictive performance of several ML models in a multiple-input multiple-output (MIMO) setting that allows incorporating the cross-correlations between the inputs. We compare the forecast accuracy of a Gaussian process regression (GPR) model to that of different neural network architectures in a multi-step-ahead time series prediction experiment. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for concatenation.
publishDate 2017
dc.date.none.fl_str_mv 2017
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/172665
url https://hdl.handle.net/2445/172665
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://search.proquest.com/docview/2190325008?pq-origsite=gscholar&fromopenview=true#
International Journal of Computer Research, 2017, vol. 24, num. 2/3, p. 173-193
dc.rights.none.fl_str_mv (c) Nova Science Publishers, 2017
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Nova Science Publishers, 2017
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 21 p.
application/pdf
dc.publisher.none.fl_str_mv Nova Science Publishers
publisher.none.fl_str_mv Nova Science Publishers
dc.source.none.fl_str_mv Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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