TY - GEN
T1 - Exploiting Neural Networks to Enhance Trend Forecasting for Hotels Reservations
AU - Zakhary, Athanasius
AU - El Gayar, Neamat
AU - Ahmed, Sanaa El Ola H.
PY - 2010
Y1 - 2010
N2 - Hotel revenue management is perceived as a managerial tool for room revenue maximization. A typical revenue management system contains two main components: Forecasting and Optimization. A forecasting component that gives accurate forecasts is a cornerstone in any revenue management system. It simply draws a good picture for the future demand. The output of the forecast component is then used for optimization and allocation in such a way that maximizes revenue. This shows how it is important to have a reliable and precise forecasting system. Neural Networks have been successful in forecasting in many fields. In this paper, we propose the use of NN to enhance the accuracy of a Simulation based Forecasting system, that was developed in an earlier work. In particular a neural network is used for modeling the trend component in the simulation based forecasting model. In the original model, Holt's technique was used to forecast the trend. In our experiments using real hotel data we demonstrate that the proposed neural network approach outperforms the Holt's technique. The proposed enhancement also resulted in better arrivals and occupancy forecasting when incorporated in the simulation based forecasting system.
AB - Hotel revenue management is perceived as a managerial tool for room revenue maximization. A typical revenue management system contains two main components: Forecasting and Optimization. A forecasting component that gives accurate forecasts is a cornerstone in any revenue management system. It simply draws a good picture for the future demand. The output of the forecast component is then used for optimization and allocation in such a way that maximizes revenue. This shows how it is important to have a reliable and precise forecasting system. Neural Networks have been successful in forecasting in many fields. In this paper, we propose the use of NN to enhance the accuracy of a Simulation based Forecasting system, that was developed in an earlier work. In particular a neural network is used for modeling the trend component in the simulation based forecasting model. In the original model, Holt's technique was used to forecast the trend. In our experiments using real hotel data we demonstrate that the proposed neural network approach outperforms the Holt's technique. The proposed enhancement also resulted in better arrivals and occupancy forecasting when incorporated in the simulation based forecasting system.
UR - http://www.scopus.com/inward/record.url?scp=77952348260&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-12159-3_22
DO - 10.1007/978-3-642-12159-3_22
M3 - Conference contribution
AN - SCOPUS:77952348260
SN - 9783642121586
T3 - Lecture Notes in Computer Science
SP - 241
EP - 251
BT - Artificial Neural Networks in Pattern Recognition. ANNPR 2010
PB - Springer
T2 - 4th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition 2010
Y2 - 11 April 2010 through 13 April 2010
ER -