A comprehensive approach to enhancing short-term hotel cancellation forecasts through dynamic machine learning models

Bibliographic Data

ID21689718
AuthorsApostolos Ampountolas (0000-0003-3992-6663, Boston University, corresponding author), Mark Legg (0000-0002-5172-9964, Penn State Berks)
Year2026
Volume32
Issue2
Pages321-341
Publication date2026-03-01
Open AccessYes
TypeARTICLE
VenueTourism Economics (JOURNAL)
Journal identifiersISSN: 1354-8166 • E-ISSN: 2044-0375
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/13548166251318768
OpenAlexW4407237057
LanguageEN
Citations received2
References cited42

Enhancing the accuracy of short-term forecasts for cancellation rates offers revenue managers the opportunity to formulate a pricing strategy for the upcoming day, yielding favorable economic outcomes. This study proposes a methodology based on dynamic models utilizing machine learning methods such as LightGBM, XGBoost, Random Forest, and ANN-MLP, highlighting the importance of data dimensionality reduction while using higher-performing variables to improve predictive accuracy and stability. Notably, lagged variables within a few days of the forecasted date and reservations made through OTAs and BAR-rated reservations exhibit significant predictive power. The results indicate that artificial neural network multi-layer perceptron (ANN-MLP) outperforms other models, especially in longer forecast horizons. The study recommends adaptable strategies considering historical data and temporal trends and leveraging ANN-MLP for superior accuracy. The findings offer valuable insights for industry practitioners, providing a nuanced understanding of cancellation patterns and suggesting strategies to optimize cancellation prediction models in a competitive marketplace

Econometrics · Economics · Geography · Tourism · Computer Science · Consumer Market Behavior and Pricing · Energy Load and Power Forecasting · Housing Market and Economics

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