Abstract
Hospitality industry plays a crucial role in the development of tourism. Predicting the future demand of a hotel is a key step in the process of hotel revenue management. Hotel passenger flow prediction plays an important role in guiding the formulation of hotel pricing and operating strategies. On the one hand, hotel passenger flow prediction can provide decision support for hotel managers and effectively avoid the waste of hotel resources and loss of revenue caused by the loss of customers. On the other hand, it is the guarantee of the priority occupation of business opportunities by hotel enterprises, which can help hotel enterprises adjust their operation strategies reasonably to better adapt to the market situation. In addition, hotel passenger flow prediction is helpful to judge the overall operating condition of the hotel industry and assess the risk level of the hotel project to be built. Hotel passenger flow is affected by many factors, such as weather, environment, season, holidays, economy, and emergencies, and has the characteristics of complex nonlinear fluctuation. The existing demand predicting methods include linear methods and nonlinear methods. The linear prediction methods rely on the stability of environment and time series, so they cannot completely simulate the complex nonlinear fluctuations characteristics of hotel passenger flow. Traditional nonlinear prediction methods need to improve the prediction accuracy, and they are difficult to deal with the increasing data of hotel passenger flow. Based on the above analysis, this paper constructs a deep learning prediction model based on Long Short-Term Memory (LSTM) to predict the number of actual monthly arrival bookings. The number of actual monthly arrival bookings can reflect the actual monthly passenger flow of a hotel. The prediction model can effectively reduce the loss caused by cancellation or nonarrival of bookings due to various reasons and improve the hotel revenue. The experimental part of this paper is based on the booking demand dataset of a resort hotel in Portugal from July 1, 2015, to August 31, 2017. Artificial neural network (ANN) and support vector regression (SVR) are built as benchmark models to predict the number of actual monthly arrival bookings of this hotel. The experimental results show that, compared with the benchmark models, the LSTM model can effectively improve the prediction ability and provide necessary reference for the hotel's future pricing decision and operation mode arrangement.
Highlights
Hospitality industry plays a crucial role in the development of tourism
Hotel passenger flow is affected by many factors, such as weather, environment, season, holidays, economy, and emergencies, and has the characteristics of complex nonlinear fluctuation. e existing demand predicting methods include linear methods and nonlinear methods. e linear prediction methods rely on the stability of environment and time series, so they cannot completely simulate the complex nonlinear fluctuations characteristics of hotel passenger flow
Based on the above analysis, this paper constructs a deep learning prediction model based on Long Short-Term Memory (LSTM) to predict the number of actual monthly arrival bookings. e number of actual monthly arrival bookings can reflect the actual monthly passenger flow of a hotel. e prediction model can effectively reduce the loss caused by cancellation or nonarrival of bookings due to various reasons and improve the hotel revenue. e experimental part of this paper is based on the booking demand dataset of a resort hotel in Portugal from July 1, 2015, to August 31, 2017
Summary
As early as the 1960s, experts and scholars had already begun researches on demand prediction methods [8]. e existing demand prediction methods include linear and nonlinear methods [6]. ese methods can be divided into four categories: classical time-series methods, econometric methods, machine learning methods, and comprehensive prediction methods [8, 9]. The linear model relies on the stability of the time series and the economic environment, and it is difficult to effectively simulate the nonlinear characteristics of hotel demand. (1) ey rely too much on the stability of environment and time series (2) Characteristics of complex nonlinear fluctuations of hotel passenger flow cannot be extracted automatically (3) ey cannot deal with a large amount of sample data in practical application (4) It is incomplete and uncertain to estimate hotel passenger flow based on customer search data. Based on the above analysis, we use the historical booking demand dataset of a resort hotel in Portugal [27] and establish a deep learning model of hotel passenger flow prediction based on LSTM to predict the number of actual monthly arrival bookings of this hotel. If the number of bookings that arrive at the hotel can be estimated in advance, it can help the hotel to prejudge the allocation of rooms, so as to make a more reasonable pricing decision and operation strategy. erefore, predicting the number of actual monthly arrival bookings has practical application value. e focus of this paper is to establish a prediction model to predict the number of actual arrival bookings in a certain month in the future
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