Publication
Decoding the future: proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis
| dc.contributor.author | Contessi, Daniele | |
| dc.contributor.author | Viverit, Luciano | |
| dc.contributor.author | Nobre Pereira, Luis | |
| dc.contributor.author | Heo, Cindy Yoonjoung | |
| dc.date.accessioned | 2024-09-04T10:14:21Z | |
| dc.date.available | 2024-09-04T10:14:21Z | |
| dc.date.issued | 2024-08 | |
| dc.description.abstract | Accurate hotel occupancy forecasting is vital for optimizing hotel revenue, yet interpretable machine learning tools lack extensive research. This paper presents a two-step approach utilizing historical and advanced booking data. Principal Components Analysis (PCA) groups similar patterns in booking curves, followed by a pickup forecasting model to predict occupancy. Evaluating the approach using real booking data from three European hotels (2018 -2022), it outperformed two benchmarks: classical additive pickup and clustering-based pickup methods. Empirical results demonstrate the superiority of PCA-based methods across all hotels and forecasting horizons. Additionally, incorporating Average Daily Rates into PCA enhances daily hotel demand forecasts, offering potential for enhanced predictions with business operational information in a low-dimensional space. | eng |
| dc.identifier.doi | 10.1016/j.ijhm.2024.103802 | |
| dc.identifier.issn | 0278-4319 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/25839 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Elsevier | |
| dc.relation | Research Centre for Tourism, Sustainability and Well-being | |
| dc.relation.ispartof | International Journal of Hospitality Management | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Hotel Demand | |
| dc.subject | Forecasting | |
| dc.subject | Machine learning | |
| dc.subject | Principal components analysis | |
| dc.subject | Additive pickup method | |
| dc.title | Decoding the future: proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Centre for Tourism, Sustainability and Well-being | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04020%2F2020/PT | |
| oaire.citation.startPage | 103802 | |
| oaire.citation.title | International Journal of Hospitality Management | |
| oaire.citation.volume | 121 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Nobre Pereira | |
| person.givenName | Luis | |
| person.identifier.ciencia-id | 6114-E329-972E | |
| person.identifier.orcid | 0000-0003-0917-7163 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| relation.isAuthorOfPublication | 090a6604-b604-414b-a8d2-c4dc731b7b51 | |
| relation.isAuthorOfPublication.latestForDiscovery | 090a6604-b604-414b-a8d2-c4dc731b7b51 | |
| relation.isProjectOfPublication | fa579efb-63c0-486e-b05d-859542b73647 | |
| relation.isProjectOfPublication.latestForDiscovery | fa579efb-63c0-486e-b05d-859542b73647 |