Predicting Hotel Booking Cancellations During High-Volatility Times

Pedro Silvestre and Nuno António ()
Additional contact information
Pedro Silvestre: Universidade Nova de Lisboa
Nuno António: Universidade Nova de Lisboa

A chapter in Information and Communication Technologies in Tourism 2025, 2025, pp 363-373 from Springer

Abstract: Abstract Like 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.

Keywords: Concept drift; Crisis; Data science; Hospitality; Machine learning; Predictive modeling (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-83705-0_30

Ordering information: This item can be ordered from
http://www.springer.com/9783031837050

DOI: 10.1007/978-3-031-83705-0_30

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().