In the hospitality industry, demand forecast accuracy is highly impacted by booking cancellations, which makes demand-management 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.

The ordered probit model is used to examine the contribution of several factors to the injury severity faced by motor-vehicle occupants involved in road accidents. The estimated results suggest that motor-vehicle occupants travelling in light-vehicles, at two-way roads, and on dry road surfaces tend to suffer more severe injuries than those who travel in heavy-vehicles, at one-way roads, and on wet road surfaces. Additionally, the driver's seat is clearly the safest seating position, urban areas seem to originate less serious accidents than rural areas, and women tend to be more likely to suffer serious or fatal injuries than men

Measuring the electrical consumption of individual appliances in a household has recently received renewed interest in the area of energy efficiency research and sustainable development. The unambiguous acquisition of information by a single monitoring point of the whole house's electrical signal is known as energy disaggregation or nonintrusive load monitoring. A novel way to look into the issue of energy disaggregation is to interpret it as a single-channel source separation problem. To this end, we analyze the performance of source modeling based on multiway arrays and the corresponding decomposition or tensor factorization. First, with the proviso that a tensor composed of the data for the several devices in the house is given, nonnegative tensor factorization is performed in order to extract the most relevant components. Second, the outcome is later embedded in the test step, where only the measured consumption over the whole home is available. Finally, the disaggregated data by the device is obtained by factorizing the associated matrix considering the learned models. In this paper, we compare this method with a recent approach based on sparse coding. The results are obtained using real-world data from household electrical consumption measurements. The analysis of the comparison results illustrates the relevance of the multiway array-based approach in terms of accurate disaggregation, as further endorsed by the statistical analysis performed.

This data article describes two datasets with hotel demand data. One of the hotels (H1) is a resort hotel and the other is a city hotel (H2). Both datasets share the same structure, with 31 variables describing the 40,060 observations of H1 and 79,330 observations of H2. Each observation represents a hotel booking. Both datasets comprehend bookings due to arrive between the 1st of July of 2015 and the 31st of August 2017, including bookings that effectively arrived and bookings that were canceled. Since this is hotel real data, all data elements pertaining hotel or costumer identification were deleted. Due to the scarcity of real business data for scientific and educational purposes, these datasets can have an important role for research and education in revenue management, machine learning, or data mining, as well as in other fields.