|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|1009137||938833||2016||11 صفحه PDF||ندارد||دانلود کنید|
• Effective revenue management decisions need accurate hotel daily demand forecasts.
• Hotels’ daily demand time series usually exhibit complex seasonal patterns.
• Methods able to handle seasonal complexities were applied to hotel time series.
• Different forecast accuracy measures may generate inconsistent results.
• The TBATS model has potential to improve accuracy of hotels’ daily forecasts.
Revenue management is a key tool for hotel managers’ decision-making process. Cutting-edge revenue management systems have been developed to support managers’ decisions and all have as an essential component an accurate forecasting module. This paper aims to introduce new time series forecasting models to be considered as a tool for forecasting daily hotel occupancies. These models were developed in a state space modelling framework which is capable of tackling seasonal complexities such as multiple seasonal periods and non-integer seasonality. An empirical study was carried out to illustrate how a practitioner may apply and compare the performance of different models when forecasting a hotel’s daily occupancy. Results showed that the trigonometric model based on the new modelling framework generally outperformed the majority of the other models. These findings are potentially useful to the entire revenue management community facing the challenge of accurately forecasting a hotel’s daily demand.
Journal: International Journal of Hospitality Management - Volume 58, September 2016, Pages 13–23