Browsing by Author "Viverit, Luciano"
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- Application of machine learning to cluster hotel booking curves for hotel demand forecastingPublication . Viverit, Luciano; Heo, Cindy Yoonjoung; Pereira, Luis; Tiana, GuidoAccurate demand forecasting is integral for data-driven revenue management decisions of hotels, but an un-precedented demand environment caused by COVID-19 pandemic has made the forecasting process more difficult. This study aims to propose a new approach for daily hotel demand forecasting by using clusters of stay dates generated from historical booking data. This new approach is fundamentally different from traditional forecasting approaches for hotels that assume the booking curves and patterns tend to be similar during the trailing period approach. In this study, historical booking curves are clustered by a machine learning algorithm using an auto-regressive manner and the additive pickup model is used to forecast daily occupancy up to 8 weeks. The efficacy of a new forecasting approach is tested using real hotel booking data of three hotels and results show that forecasts of hotel demand are more accurate when they are generated at cluster-level for all forecasting horizons.
- Decoding the future: proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysisPublication . Contessi, Daniele; Viverit, Luciano; Nobre Pereira, Luis; Heo, Cindy YoonjoungAccurate 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.
- Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotelsPublication . Heo, Cindy Yoonjoung; Viverit, Luciano; Pereira, LuisDemand forecast accuracy is critical for hotels to operate their properties efciently and proftably. The COVID-19 pandemic is a massive challenge for hotel demand forecasting due to the relevance of historical data. Therefore, the aims of this study are twofold: to present an extension of the additive pickup method using time series and moving averages; and to test the model using the real reservation data of a hotel in Italy during the COVID-19 pandemic. This study shows that historical data are still useful for a SME hotel amid substantial demand uncertainty caused by COVID-19. Empirical results suggest that the proposed method performs better than the classical one, particularly for longer forecasting horizons and for periods when the hotel is not fully occupied.