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In attempting to minimize losses, hotels tend to implement restrictive cancellation policies and employ overbooking tactics, which, in turn, reduce the number of bookings and reduce revenue. To tackle the uncertainty arising from booking cancellations, we combined the data from eight hotels\u2019 property management systems with data from several sources (weather, holidays, events, social reputation, and online prices\/inventory) and machine learning interpretable algorithms to develop booking cancellation prediction models for the hotels. In a real production environment, improvement of the forecast accuracy due to the use of these models could enable hoteliers to decrease the number of cancellations, thus, increasing confidence in demand-management decisions. Moreover, this work shows that improvement of the demand forecast would allow hoteliers to better understand their net demand, that is, current demand minus predicted cancellations. Simultaneously, by focusing not only on forecast accuracy but also on its explicability, this work illustrates one other advantage of the application of these types of techniques in forecasting: the interpretation of the predictions of the model. By exposing cancellation drivers, models help hoteliers to better understand booking cancellation patterns and enable the adjustment of a hotel\u2019s cancellation policies and overbooking tactics according to the characteristics of its bookings. <\/jats:p>","DOI":"10.1177\/1938965519851466","type":"journal-article","created":{"date-parts":[[2019,5,29]],"date-time":"2019-05-29T08:31:46Z","timestamp":1559118706000},"page":"298-319","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":80,"title":["Big Data in Hotel Revenue Management: Exploring Cancellation Drivers to Gain Insights Into Booking Cancellation Behavior"],"prefix":"10.1177","volume":"60","author":[{"given":"Nuno","family":"Antonio","sequence":"first","affiliation":[{"name":"ISCTE \u2013 Instituto Universit\u00e1rio de Lisboa, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Lisboa, Portugal"}]},{"given":"Ana","family":"de Almeida","sequence":"additional","affiliation":[{"name":"ISCTE \u2013 Instituto Universit\u00e1rio de Lisboa, Portugal"},{"name":"Centre for Informatics and Systems of the University of Coimbra, Lisboa, Portugal"},{"name":"ISTAR, Lisboa, Portugal"}]},{"given":"Luis","family":"Nunes","sequence":"additional","affiliation":[{"name":"ISCTE \u2013 Instituto Universit\u00e1rio de Lisboa, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Lisboa, Portugal"},{"name":"ISTAR, Lisboa, Portugal"}]}],"member":"179","published-online":{"date-parts":[[2019,5,29]]},"reference":[{"key":"bibr1-1938965519851466","volume-title":"Applied predictive analytics: Principles and techniques for the professional data analyst","author":"Abbott D.","year":"2014"},{"key":"bibr2-1938965519851466","first-page":"4","volume":"12","author":"Anderson C. 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