Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis
提出一种结合主成分分析和拾取预测模型的两步法，利用历史与预订数据预测酒店入住率，在三个欧洲酒店2018-2022年数据上优于传统方法，并发现加入平均每日房价可提升预测效果。
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.