Publication
Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotels
| dc.contributor.author | Heo, Cindy Yoonjoung | |
| dc.contributor.author | Viverit, Luciano | |
| dc.contributor.author | Pereira, Luis | |
| dc.date.accessioned | 2023-07-04T13:17:23Z | |
| dc.date.available | 2023-07-04T13:17:23Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Demand forecast accuracy is critical for hotels to operate their properties efciently and proftably. The COVID-19 pandemic is a massive challenge for hotel demand forecasting due to the relevance of historical data. Therefore, the aims of this study are twofold: to present an extension of the additive pickup method using time series and moving averages; and to test the model using the real reservation data of a hotel in Italy during the COVID-19 pandemic. This study shows that historical data are still useful for a SME hotel amid substantial demand uncertainty caused by COVID-19. Empirical results suggest that the proposed method performs better than the classical one, particularly for longer forecasting horizons and for periods when the hotel is not fully occupied. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1057/s41272-023-00421-1 | pt_PT |
| dc.identifier.eissn | 1477-657X | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/19809 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer | pt_PT |
| dc.relation | Research Centre for Tourism, Sustainability and Well-being | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Hotel demand forecast | pt_PT |
| dc.subject | Additive pickup | pt_PT |
| dc.subject | Time series | pt_PT |
| dc.subject | COVID-19 pandemic | pt_PT |
| dc.subject | Small and medium-sized enterprises (SMEs) hotels | pt_PT |
| dc.subject | Revenue management | pt_PT |
| dc.title | Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotels | pt_PT |
| 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.title | Journal of Revenue and Pricing Management | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| 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 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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