An introduction to helpful forecasting methods for hotel revenue management

Abstract 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.

[1]  E. S. Gardner.  EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[2]  Peter R. Winters,et al.  Forecasting Sales by Exponentially Weighted Moving Averages , 1960 .

[3]  Y. Poon,et al.  Analyzing the Use of an Advance Booking Curve in Forecasting Hotel Reservations , 2015 .

[4]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[5]  Zvi Schwartz,et al.  Hotel daily occupancy forecasting with competitive sets: a recursive algorithm , 2016 .

[6]  Rob J Hyndman,et al.  A state space framework for automatic forecasting using exponential smoothing methods , 2002 .

[7]  J. W. Taylor,et al.  Short-term electricity demand forecasting using double seasonal exponential smoothing , 2003, J. Oper. Res. Soc..

[8]  David A. Cranage,et al.  Forecasting Hotel Occupancy Rates with Time Series Models: An Empirical Analysis , 1990 .

[9]  Ger Koole,et al.  Booking horizon forecasting with dynamic updating: A case study of hotel reservation data , 2011 .

[10]  Mounir Ben Ghalia,et al.  Forecasting uncertain hotel room demand , 2001, Inf. Sci..

[11]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[12]  Bing Pan,et al.  Forecasting hotel room demand using search engine data. , 2012 .

[13]  Z. Schwartz,et al.  Improving the Accuracy of Hotel Reservations Forecasting: Curves Similarity Approach , 1997 .

[14]  Rob Law,et al.  Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique , 2004 .

[15]  K. Talluri,et al.  The Theory and Practice of Revenue Management , 2004 .

[16]  Soulaymane Kachani,et al.  Forecasting and optimisation for hotel revenue management , 2007 .

[17]  Sedat Yüksel.  An integrated forecasting approach to hotel demand , 2007, Math. Comput. Model..

[18]  Z. Schwartz,et al.  On revenue management and the use of occupancy forecasting error measures , 2014 .

[19]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[20]  Athanasius Zakhary,et al.  Forecasting hotel arrivals and occupancy using Monte Carlo simulation , 2011 .

[21]  Kevin K. F. Wong,et al.  Tourism forecasting: To combine or not to combine? , 2007 .

[22]  Haiyan Song,et al.  Tourism demand modelling and forecasting—A review of recent research , 2008 .

[23]  Basak Denizci Guillet,et al.  Revenue Management for the Hospitality Industry , 2010 .

[24]  Rob J Hyndman,et al.  Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing , 2011 .

[25]  James W. Taylor,et al.  Triple seasonal methods for short-term electricity demand forecasting , 2010, Eur. J. Oper. Res..

[26]  Wen-Chyuan Chiang,et al.  An overview of research on revenue management: current issues and future research , 2007 .

[27]  T. Evgeniou,et al.  To combine or not to combine: selecting among forecasts and their combinations , 2005 .

[28]  Michael McAleer,et al.  Forecasting h(m)otel guest nights in New Zealand , 2009 .

[29]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[30]  C. Lim,et al.  An econometric analysis of hotel–motel room nights in New Zealand with stochastic seasonality , 2011 .

[31]  Sheryl E. Kimes,et al.  A comparison of forecasting methods for hotel revenue management , 2003 .

[32]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[33]  James W. Taylor.  Triple Seasonal Methods for Short-term Load Forecasting , 2010 .