Exploiting Neural Networks to Enhance Trend Forecasting for Hotels Reservations

  • Athanasius Zakhary
  • Neamat El Gayar
  • Sanaa El-Ola. H. Ahmed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5998)


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.


Hide Node Forecast Method Forecast Horizon Trend Component Revenue Management 
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  1. 1.
    Talluri, K.T., Van Ryzin, G.J.: The Theory and Practice of Revenue Management. Springer Science+Buisness Media, Inc. (2005)Google Scholar
  2. 2.
    Ingold, A., McMahon-Beattie, U., Yeoman, I. (eds.): Yield Management. Continuum, 2nd edn. (2003)Google Scholar
  3. 3.
    Weatherford, L.R., Kimes, S.E.: A comparison of forecasting methods for hotel revenue management. International Journal of Forecasting 99, 401–415 (2003)CrossRefGoogle Scholar
  4. 4.
    Zakhary, A., El Gayar, N., Atiya, A.F.: A comparative study of the pickup method and its variations using a simulated hotel reservation data. ICGST International Journal on Artificial Intelligence and Machine Learning 8, 15–21 (2008)Google Scholar
  5. 5.
    Zakhary, A., Atyia, A., El-Shishiny, H., El Gayar, N.: Forecasting hotel arrivals and occupancy using monte carlo simulation. Journal of Revenue and Pricing Management (to appear)Google Scholar
  6. 6.
    Frechtling, D.: Forecasting Tourism Demand: Methods and Strategies. Butterworth Heinemann, Oxford (2001)Google Scholar
  7. 7.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1999)zbMATHGoogle Scholar
  8. 8.
    Darbellay, G.A., Slama, M.: Forecasting the short-term demand for electricity: Do neural networks stand a better chance? International Journal of Forecasting 16, 71–83 (2000)CrossRefGoogle Scholar
  9. 9.
    Gooijer, J.G.D., Hyndman, R.J.: 25 years of IIF time series forecasting: A selective review. Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics (2005)Google Scholar
  10. 10.
    Kline, D.M., Zhang, G.P.: Methods for multi-step time series forecasting with neural networks. Neural Networks for Business Forecasting, 226–250 (2004)Google Scholar
  11. 11.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings International Joint Conference on Artificial Intelligence, IJCAI (1995)Google Scholar
  12. 12.
    Sandy, J.K.O., Balkin, D.: Automatic neural network modeling for univariate time series. International Journal of Forecasting 16(4), 509–515 (2000)CrossRefGoogle Scholar
  13. 13.
    Gardner, E.S.: Exponential smoothing: The state of the art Part II. International Journal of Forecasting 22, 637–666 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Athanasius Zakhary
    • 1
  • Neamat El Gayar
    • 1
    • 2
  • Sanaa El-Ola. H. Ahmed
    • 1
  1. 1.Faculty of Computers and InformationCairo UniversityGizaEgypt
  2. 2.Center for Informatics Science, School of Communication and, Information TechnologyNile UniversityGizaEgypt

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