Predicting Hotel Demand with ML: Leveraging Historical Bookings, OTA Trend and Local Events

Abstract Accurate demand forecasting is essential for hotels to optimize pricing strategies, manage inventory, and enhance operational efficiency. This paper presents a machine learning-based approach to hotel demand forecasting that integrates multiple data sources, including historical booking data, real-time insights from OTA platform (e.g., Booking.com), and local event data. The forecasting model employs a range of algorithms, specifically SARIMA, Random Forest, and LSTM networks, to predict future demand patterns with improved accuracy. By leveraging dynamic booking trends, seasonality, and the influence of nearby events, the proposed model surpasses traditional forecasting methods in predictive accuracy. The findings highlight that incorporating external data—such as event schedules and OTA demand—significantly enhances forecasting performance, enabling hotels to make more data-driven decisions regarding pricing and resource management. This study demonstrates the transformative potential of machine learning in hotel demand forecasting, offering actionable insights that empower hotels to optimize revenue and elevate guest satisfaction.