| dc.description.abstract | In the hospitality industry, demand forecast accuracy is highly impacted by booking cancellations, which makes demandmanagement
decisions difficult and risky. 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’ 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’s cancellation policies and overbooking tactics according to the
characteristics of its bookings. | vi |
