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Predicting Hotel Booking Cancellations During High-Volatility Times

2025-05-27Documento de conferência Acesso aberto
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dc.contributor.authorSilvestre, Pedro
dc.contributor.authorAntónio, Nuno
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.coverage.spatialGewerbestrasse, Cham, Switzerland
dc.date.accessioned2026-01-14T19:44:05Z
dc.date.available2026-01-14T19:44:05Z
dc.date.embargoedUntil2026-05-27
dc.date.issued2025-05-27
dc.descriptionSilvestre, P., & António, N. (2025). Predicting Hotel Booking Cancellations During High-Volatility Times. In L. Nixon, A. Tuomi, & P. O'Connor (Eds.), Information and Communication Technologies in Tourism 2025: Proceedings of the ENTER 2025 eTourism Conference, Wroclaw, Poland, February 17–21 (pp. 363-373). (Springer Proceedings in Business and Economics). Springer Nature. https://doi.org/10.1007/978-3-031-83705-0_30 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020 (DOI: https://doi.org/10.54499/UIDB/04152/2020)—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).
dc.description.abstractLike in other service industries, booking cancellations impact hotel management decisions, negatively contributing to accurate forecasts. Previous research showed it is possible to develop predictive models using booking data. However, existing models did not consider high-volatile times, such as a pandemic, where mass cancellations happen. This research uses datasets from four hotels to assess in a first study how existing machine learning classification models perform under the conditions imposed by high-volatility times (COVID-19 pandemic). In a second study, this research studies how models can be improved using a sliding window training approach. Results show that existing booking cancellation models can be improved if a sliding window with nine months of training data is used, with performance increasing up to 5% points in terms of Area Under the Curve. The findings from both studies demonstrate that while pre-pandemic models remain effective, incorporating pandemic data using a sliding window approach significantly improves predictive accuracy.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent11
dc.format.extent666582
dc.identifier.doi10.1007/978-3-031-83705-0_30
dc.identifier.isbn978-3-031-83704-3
dc.identifier.isbn978-3-031-83705-0
dc.identifier.issn2198-7246
dc.identifier.otherPURE: 119108613
dc.identifier.otherPURE UUID: d06de1f9-cbbb-4557-915c-86c0ca53e183
dc.identifier.otherScopus: 105007234934
dc.identifier.urihttp://hdl.handle.net/10362/199153
dc.identifier.urlhttps://www.scopus.com/pages/publications/105007234934
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationhttps://doi.org/10.54499/UID/PRR/04152/2025
dc.subjectConcept drift
dc.subjectCrisis
dc.subjectData science
dc.subjectHospitality
dc.subjectMachine learning
dc.subjectPredictive modeling
dc.subjectGeneral Business,Management and Accounting
dc.subjectEconomics, Econometrics and Finance(all)
dc.titlePredicting Hotel Booking Cancellations During High-Volatility Timesen
dc.typeconference object
degois.publication.firstPage363
degois.publication.lastPage373
degois.publication.titleInformation and Communication Technologies in Tourism 2025
degois.publication.title32nd ENTER International eTourism Conference 2025
dspace.entity.typePublication
rcaap.rightsopenAccess

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