Forecasting hotel reservations with long short-term memory-based recurrent neural networks

Abstract

Hotel reservations tie up a much large portion of a hotels annual revenue. It is of great benefit for hotel managers to accurately forecast the numbers of reservations each day so that they can make better operational and tactical decisions. In this study, we review three types of forecasting methods commonly used in practice and briefly illustrate the concepts of neural networks. We then propose two long short-term memory (LSTM) models based on recurrent neural networks. Actual reservations of four hotels in the USA are used to estimate and test the proposed two models. To measure the relative performance, six machine learning (ML) models of decision tree, multilayer perceptron, lasso, linear regression, random forest, and ridge are also estimated and tested against the same datasets. The empirical results show that, on average, the forecasting accuracy of using LSTM models has been improved about 3.0% over that of using the best of the ML models.