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Forecasting hotel demand for revenue management using machine learning regression methods

2021Journal article Restricted access
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dc.contributor.authorPereira, Luis
dc.contributor.authorCerqueira, Vitor
dc.date.accessioned2022-02-18T14:53:08Z
dc.date.available2022-02-18T14:53:08Z
dc.date.issued2021
dc.description.abstractThis paper compares the accuracy of a set of 22 methods for short-term hotel demand forecasting for lead times up to 14 days ahead. Machine learning models are compared with methods ranging from seasonal naive to exponential smoothing methods for double seasonality. The machine learning methods considered include a new approach based on arbitrating, in which several forecasting models are dynamically combined to obtain predictions. Arbitrating is a metalearning approach that combines the output of experts according to predictions of the loss that they will incur. Particularly, the dynamic ensemble method is used. The methods were compared using a real time series of daily demand for a four-star hotel located in the south of Europe. The forecasting performance of those methods was assessed using three alternative accuracy measures. Results from extensive empirical experiments led us to conclude that machine learning methods outperform traditional hotel demand forecasting methods. We found that the use of machine learning models can reduce the root mean squared error up to 54% for a 1-day forecast horizon, and up to 45% for a 14-days forecast horizon, when compared with traditional exponential smoothing methods.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1080/13683500.2021.1999397pt_PT
dc.identifier.issn1368-3500
dc.identifier.urihttp://hdl.handle.net/10400.1/17572
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherTaylor & Francispt_PT
dc.relationResearch Centre for Tourism, Sustainability and Well-being
dc.relationTime series forecasting
dc.subjectForecastingpt_PT
dc.subjectMachine learningpt_PT
dc.subjectHotel demandpt_PT
dc.subjectRevenue managementpt_PT
dc.titleForecasting hotel demand for revenue management using machine learning regression methodspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre for Tourism, Sustainability and Well-being
oaire.awardTitleTime series forecasting
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04020%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F135705%2F2018/PT
oaire.citation.endPage18pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleCurrent Issues in Tourismpt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameNobre Pereira
person.givenNameLuis
person.identifier.ciencia-id6114-E329-972E
person.identifier.orcid0000-0003-0917-7163
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscovery090a6604-b604-414b-a8d2-c4dc731b7b51
relation.isProjectOfPublicationfa579efb-63c0-486e-b05d-859542b73647
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