A comparison of forecasting methods for hotel revenue management
By L. Weatherford, Sheryl E. Kimes. 2003
302 citations recorded by the source corpus.
Abstract The arrivals forecast is one of the key inputs for a successful hotel revenue management system, but no research on the best forecasting method has been conducted. In this research, we used data from Choice Hotels and Marriott Hotels to test a variety of forecasting methods and to determine the most accurate method. Preliminary results using the Choice Hotel data show that pickup methods and regression produced the lowest error, while the booking curve and combination forecasts produced fairly inaccurate results. The more in-depth study using the Marriott Hotel data showed that exponential smoothing, pickup, and moving average models were the most robust.
Subfields: Management Science and Operations Research, Marketing
View Paper: https://www.semanticscholar.org/paper/609bf188ed3e27ac8f3769417ce4b96c0f8ffb6f
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In Management Science and Operations Research
In Marketing
Citations in the corpus
- Forecasting Chinese tourist volume with search engine data (2015, methodology): “…other type consists of artificial intelligence methods, such as artificial neural network, grey theory, rough set theory, fuzzy theory, genetic algorithm,Monte Carlo simulation, and expert systems (Abratt, Nel, & Nezer, 1995; Andrew, Cranage, & Lee, 1990; Law & Au, 1999; Weatherford & Kimes, 2003).”
- Hotel Revenue Management – A Critical Literature Review (2011, unknown): “As combined methods Weatherford and Kimes (2003) identify regression models (Burger et al., 2001; Chen & Kachani, 2007; Weatherford & Kimes, 2003) and weighted average between historical and advanced booking forecasts (Chen & Kachani, 2007).”