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Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis
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DOI：10.1016/j.ijhm.2024.103802.png)
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Accurate hotel occupancy forecasting is vital for optimizing hotel revenue, yet interpretable machine learning tools lack extensive research. This paper presents a two-step approach utilizing historical and advanced booking data. Principal Components Analysis (PCA) groups similar patterns in booking curves, followed by a pickup forecasting model to predict occupancy. Evaluating the approach using real booking data from three European hotels (2018 -2022), it outperformed two benchmarks: classical additive pickup and clustering-based pickup methods. Empirical results demonstrate the superiority of PCA-based methods across all hotels and forecasting horizons. Additionally, incorporating Average Daily Rates into PCA enhances daily hotel demand forecasts, offering potential for enhanced predictions with business operational information in a low-dimensional space.
Keyword:
Hotel Demand Forecasting
Machine learning
Principal components analysis
Additive pickup method
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被引数:
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