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A systematic review of machine learning applications in hotel occupancy forecasting Cover

A systematic review of machine learning applications in hotel occupancy forecasting

Open Access
|Dec 2025

Figures & Tables

Figure 1:

Article Screening Protocol
Article Screening Protocol

Figure 2:

Bibliometric Characteristics
Bibliometric Characteristics

Figure 3:

Study Characterisations
Study Characterisations

Figure 4:

Characteristics of Input Datasets
Characteristics of Input Datasets

Figure 5:

Forecasting Horizons Used
Forecasting Horizons Used

Figure 6:

Machine Learning Methodologies Used in the Studies
Machine Learning Methodologies Used in the Studies

Figure 7:

Machine Learning Pipeline Methodologies for Cross Validation (CV), Accuracy Metrics, Hyperparameter Tuning, and Interpretability
Machine Learning Pipeline Methodologies for Cross Validation (CV), Accuracy Metrics, Hyperparameter Tuning, and Interpretability

j_ejthr-2025-0022_tab_001

Context HotelIntervention Machine LearningMechanism Demand ForecastingOutcome Occupancy
“hotel*”“machine learning”“demand forecast*”“occupancy rate”
“hospitality”“ML”“occupancy”“occup*”
“resort”“artificial intelligence”“demand model*”“room rate”
“lodg*”“AI”“predict*”“vacanc*”
“accomodat*”“deep learning”“demand”
“computational intelligence”“model*”
“CI”
DOI: https://doi.org/10.2478/ejthr-2025-0022 | Journal eISSN: 2182-4924 | Journal ISSN: 2182-4916
Language: English
Page range: 311 - 327
Submitted on: Dec 18, 2024
|
Accepted on: Mar 28, 2025
|
Published on: Dec 31, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2025 Ismael Gómez-Talal, Mana Azizsoltani, Jared Bischoff, Kasra Ghaharian, Ashok Singh, published by Polytechnic Institute of Leiria
This work is licensed under the Creative Commons Attribution 4.0 License.