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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Antonio, N., de Almeida, A. & Nunes, L. (2017). Predicting hotel booking cancellations to decrease uncertainty and increase revenue. Tourism and Management Studies. 13 (2), 25-39
Exportar Referência (IEEE) 
N. M. António et al.,  "Predicting hotel booking cancellations to decrease uncertainty and increase revenue", in Tourism and Management Studies, vol. 13, no. 2, pp. 25-39, 2017
Exportar BibTeX 
@article{antónio2017_1715049449878,
	author = "Antonio, N. and de Almeida, A. and Nunes, L.",
	title = "Predicting hotel booking cancellations to decrease uncertainty and increase revenue",
	journal = "Tourism and Management Studies",
	year = "2017",
	volume = "13",
	number = "2",
	doi = "10.18089/tms.2017.13203",
	pages = "25-39",
	url = "http://tmstudies.net/index.php/ectms/article/view/1000"
}
Exportar RIS 
TY  - JOUR
TI  - Predicting hotel booking cancellations to decrease uncertainty and increase revenue
T2  - Tourism and Management Studies
VL  - 13
IS  - 2
AU  - Antonio, N.
AU  - de Almeida, A.
AU  - Nunes, L.
PY  - 2017
SP  - 25-39
SN  - 2182-8458
DO  - 10.18089/tms.2017.13203
UR  - http://tmstudies.net/index.php/ectms/article/view/1000
AB  - Booking cancellations have a substantial impact in demand-management decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation.
Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%.  This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled.
Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies.
ER  -