ABSTRACT
Accurate demand forecasting is integral for data-driven revenue management decisions of hotels, but an unprecedented demand environment caused by COVID-19 pandemic has made the forecasting process more difficult. This study aims to propose a new approach for daily hotel demand forecasting by using clusters of stay dates generated from historical booking data. This new approach is fundamentally different from traditional forecasting approaches for hotels that assume the booking curves and patterns tend to be similar during the trailing period approach. In this study, historical booking curves are clustered by a machine learning algorithm using an auto-regressive manner and the additive pickup model is used to forecast daily occupancy up to 8 weeks. The efficacy of a new forecasting approach is tested using real hotel booking data of three hotels and results show that forecasts of hotel demand are more accurate when they are generated at cluster-level for all forecasting horizons. © 2023 Elsevier Ltd
ABSTRACT
Demand forecast accuracy is critical for hotels to operate their properties efficiently and profitably. The COVID-19 pandemic is a massive challenge for hotel demand forecasting due to the relevance of historical data. Therefore, the aims of this study are twofold: to present an extension of the additive pickup method using time series and moving averages;and to test the model using the real reservation data of a hotel in Italy during the COVID-19 pandemic. This study shows that historical data are still useful for a SME hotel amid substantial demand uncertainty caused by COVID-19. Empirical results suggest that the proposed method performs better than the classical one, particularly for longer forecasting horizons and for periods when the hotel is not fully occupied. © 2023, The Author(s).