Forecasting Hotel Demand Using Stacked Generalization

Author

Mana AzizsoltaniFollow

Award Date

5-1-2025

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

William F. Harrah College of Hospitality

First Committee Member

Ashok Singh

Second Committee Member

Mehmet Erdem

Third Committee Member

Kasra Ghaharian

Fourth Committee Member

Rohan Dalpatadu

Number of Pages

205

Abstract

Demand forecasting is one of the most critical functions of hotel revenue management. It serves as the bedrock for strategic and operational decision-making across all facets of a hospitality organization, including pricing, inventory management, staffing, risk management, and purchasing. In fact, the success of a hotel is largely predicated on its ability to accurately and consistently predict and adapt to future trends. Now, with the proliferation of artificial intelligence and advances in information technology, demand forecasting in hotels stands to benefit significantly. Yet, the literature has only recently started to explore these solutions. Accordingly, there is a lack of research that rigorously develops and applies cutting-edge machine learning methodologies. In this dissertation, I fill this gap by rigorously validating and evaluating the accuracy and interpretability of machine learning models as well as introducing the concept of meta-learning in the field hotel revenue management using hotel occupancy data from a large casino hotel. The models were evaluated over various forecasting horizons and accuracy metrics, resulting in a series of experiments that showed that model performance varied across forecasting horizon and between model types. In doing so, I found that the suggested meta-model had the highest performance for further-out time horizons, but that the traditional models yielded better performances at the shorter time horizons. These results suggest that forecasting is not a one-size-fits-all exercise, but rather a search for the best forecasting method for the particular market, forecasting horizon, or dataset. This phenomenon is particularly important for both academics and practitioners, as it implies that models must be built with methodological rigor and contextual awareness in order to achieve the most accurate results.

Keywords

Artificial Intelligence; Demand Forecasting; Machine Learning; Revenue Management; Stacked Generalization

Disciplines

Artificial Intelligence and Robotics | Computer Engineering | Hospitality Administration and Management | Leisure Studies | Statistics and Probability | Tourism and Travel

File Format

pdf

File Size

2400 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Repository Citation

Azizsoltani, Mana, "Forecasting Hotel Demand Using Stacked Generalization" (2025). UNLV Theses, Dissertations, Professional Papers, and Capstones. 5242.
https://oasis.library.unlv.edu/thesesdissertations/5242

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/