Demand forecasting in hospitality using smoothed demand curves

Demand forecasting is one of the fundamental components of a successful revenue management system. This paper provides a new model, which is inspired by cubic smoothing splines, resulting in smooth demand curves per rate class over time until the check-in date.This model makes a trade-off between the forecasting error and the smoothness of the fit, and is therefore able to capture natural guest behavior. The model is tested on hospitality data. We also implemented an optimization module, and computed the expected improvement using our forecast and the optimal pricing policy. Using data of four properties from a major hotel chain, between 2.9 and 10.2% more revenue is obtained than using the heuristic pricing done by the hotels. Keywords— Revenue Management, Forecasting, Cubic Smoothing Splines

[1]  Linda H. Zhao,et al.  Root-Unroot Methods for Nonparametric Density Estimation and Poisson Random-Effects Models , 2002 .

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

[3]  James F. Epperson.  On the Runge example , 1987 .

[4]  Jeppe Rich.  A spline function class suitable for demand models , 2018 .

[5]  J. Shields,et al.  Small Business Seasonality: Characteristics and Management , 2013 .

[6]  Garrett J. van Ryzin,et al.  Revenue Management Under a General Discrete Choice Model of Consumer Behavior , 2004, Manag. Sci..

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

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

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

[10]  Enric Monte,et al.  A new forecasting approach for the hospitality industry , 2015 .

[11]  Fong-Lin Chu,et al.  Forecasting tourism demand with ARMA-based methods. , 2009 .

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

[13]  M. Crossan.  Hotel revenue management: Principles and practices , 2014 .

[14]  Octavio Loyola-González,et al.  Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View , 2019, IEEE Access.

[15]  Mark Ferguson,et al.  A Comparison of Unconstraining Methods to Improve Revenue Management Systems , 2009 .

[16]  Larry Weatherford,et al.  Better unconstraining of airline demand data in revenue management systems for improved forecast accuracy and greater revenues , 2002 .

[17]  Ger Koole,et al.  Estimating unconstrained demand rate functions using customer choice sets , 2011 .

[18]  Generation of Water Demand Time Series through Spline Curves , 2020 .