Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis

Abstract

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.