Prediction accuracy for reservation-based forecasting methods applied in Revenue Management

Abstract With a few notable exceptions, airlines and hospitality forecasting research has been focused so far on point predictions of customers’ bookings. However, Revenue Management decisions are subject to a much greater risk when based exclusively on point predictions. To overcome this drawback, we propose a stochastic framework that allows the construction of prediction intervals for reservation-based (pickup) forecasting methods, which are widely used in the industry. Moreover, we introduce an extension of the multiplicative pickup technique based on Generalized Linear Models. We test the proposed framework with real reservation data from a medium-sized hotel on Lake Maggiore (Italy) and we obtain more efficient prediction intervals relative to classical time series methods. Our approach can be useful to hotel revenue managers that wish to make more informed decisions, planning alternative pricing and room allocation strategies for a range of possible demand scenarios.

[1]  I. Foroni,et al.  Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability , 2019, Sustainability.

[2]  L. Weatherford.  The history of forecasting models in revenue management , 2016 .

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

[4]  Haiyan Song,et al.  New developments in tourism and hotel demand modeling and forecasting , 2017 .

[5]  Martin J. Beckmann,et al.  Airline demand: An analysis of some frequency distributions , 1958 .

[6]  Robert L. Winkler,et al.  Combining Interval Forecasts , 2016 .

[7]  Gang Li,et al.  The combination of interval forecasts in tourism , 2019, Annals of Tourism Research.

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

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

[10]  Luis Nobre Pereira,et al.  An introduction to helpful forecasting methods for hotel revenue management , 2016 .

[11]  Rob J. Hyndman,et al.  Another Look at Forecast Accuracy Metrics for Intermittent Demand , 2006 .

[12]  Vladimir Sashov Zhechev,et al.  Hotel Revenue Management – A Critical Literature Review , 2011 .

[13]  Chris Chatfield,et al.  Time‐series forecasting , 2000 .

[14]  Douglas A. Wolfe,et al.  Nonparametric Statistical Methods , 1973 .

[15]  M. Merz,et al.  Stochastic Claims Reserving Methods in Insurance , 2008 .

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

[17]  G. Taylor,et al.  Loss Reserving: An Actuarial Perspective , 2000 .

[18]  P. Pellegrini,et al.  Are traditional forecasting models suitable for hotels in Italian cities , 2014 .

[19]  Cynthia Barnhart,et al.  Quantitative problem solving methods in the airline industry : a modeling methodology handbook , 2012 .

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

[21]  Anthony Owen Lee,et al.  Airline reservations forecasting--probabilistic and statistical models of the booking process , 1990 .

[22]  T. Mack.  The Standard Error of Chain Ladder Reserve Estimates: Recursive Calculation and Inclusion of a Tail Factor , 1999, ASTIN Bulletin.

[23]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[24]  Dimitrios Buhalis,et al.  Forecasting tourist arrivals at attractions: Search engine empowered methodologies , 2018, Tourism Economics.

[25]  Shen Liu,et al.  Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals , 2011 .

[26]  Irene C. L. Ng.  The Pricing and Revenue Management of Services - A strategic approach , 2009, Routledge Advances in Management and Business Studies.

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

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

[29]  Apostolos Ampountolas,et al.  Forecasting hotel demand uncertainty using time series Bayesian VAR models , 2018, Tourism Economics.