The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]

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: capítulo de libro
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/117730
Acceso en línea:https://hdl.handle.net/2445/117730
Access Level:acceso abierto
Palabra clave:Aprenentatge automàtic
Distribució de Gauss
Anàlisi de regressió
Previsió
Machine learning
Gaussian distribution
Regression analysis
Forecasting
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spelling The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]Clavería González, ÓscarMonte Moreno, EnricTorra Porras, SalvadorAprenentatge automàticDistribució de GaussAnàlisi de regressióPrevisióMachine learningGaussian distributionRegression analysisForecastingMachine 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 Publishers, Inc.2017info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2445/117730Llibres / Capítols de llibre (Econometria, Estadística i Economia Aplicada)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésCapítol del llibre: “Machine Learning: Advances in Research and Applications”, ISBN: 978-1-53612-570-2 Editors: Roger Inge and Jan Leif, Nova Science Publishers, Inc. 2017. pp. 59-90(c) Nova Science Publishers, Inc., 2017info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1177302026-05-27T06:46:51Z
dc.title.none.fl_str_mv The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
title The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
spellingShingle The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
Clavería González, Óscar
Aprenentatge automàtic
Distribució de Gauss
Anàlisi de regressió
Previsió
Machine learning
Gaussian distribution
Regression analysis
Forecasting
title_short The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
title_full The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
title_fullStr The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
title_full_unstemmed The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
title_sort The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
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
Previsió
Machine learning
Gaussian distribution
Regression analysis
Forecasting
topic Aprenentatge automàtic
Distribució de Gauss
Anàlisi de regressió
Previsió
Machine learning
Gaussian distribution
Regression analysis
Forecasting
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
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/acceptedVersion
format bookPart
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/117730
url https://hdl.handle.net/2445/117730
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Capítol del llibre: “Machine Learning: Advances in Research and Applications”, ISBN: 978-1-53612-570-2 Editors: Roger Inge and Jan Leif, Nova Science Publishers, Inc. 2017. pp. 59-90
dc.rights.none.fl_str_mv (c) Nova Science Publishers, Inc., 2017
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Nova Science Publishers, Inc., 2017
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Nova Science Publishers, Inc.
publisher.none.fl_str_mv Nova Science Publishers, Inc.
dc.source.none.fl_str_mv Llibres / Capítols de llibre (Econometria, Estadística i Economia Aplicada)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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