The hotel industry requires accurate demand forecasting to maximize revenue and optimize resource allocation. Other conventional forecasting methods are ineffective in situations with complex patterns, which may be influenced by additional dynamic factors such as seasonality, local events, and weather conditions. This paper presents the Hybrid Ensemble Machine Learning Demand Forecasting Algorithm (HEML-DFA), a multi-model predictive model that aims to enhance forecast accuracy through machine learning. The model uses past bookings, prices, event signals, and environmental conditions to generate robust demand projections. A variety of machine learning algorithms were trained and tested, including Random Forests, Support Vector Machines, and Neural Networks, and the optimal-performing model was selected by HEML-DFA using an ensemble-based decision mechanism. The experimental findings confirm that HEML-DFA minimizes forecast error, with an MAE of 3.98 and an RMSE of 5.62, both lower than those of the traditional baselines. These advancements indicate that sophisticated predictive analytics can enhance hotel revenue management and strategic planning. The paper concludes by highlighting the value of hybrid machine learning systems in improving operational efficiency and provides directions for incorporating real-time analytics in subsequent studies.
 

Demand Forecasting, Predictive Analytics, Machine Learning, Hotel Industry, HEML-DFA, Ensemble Models, Revenue Management