approach

In recent years, neural net-works have become popular in the scientific and business fields. In the hotel industry, researchers have recently devoted attention to the application of neural net-works to the classification of tourist segments and the prediction of visitor behav-iour. However, no previous attempt has been made to incorporate neural networks into hotel occupancy rate forecasting. This paper reports on a study about applying neural networks to the forecasting of room occu-pancy rates. The significance of this approach was tested with actual data from the Hong Kong hotel industry. Estimated room occupancy rates were compared with actual room occupancy rates. Experimental results indicate that using neural networks to forecast room occupancy rates outperforms multiple regression and naïve extrapo-lation, two commonly used forecasting approaches