Hotel Demand

Accurately predicting hotel demand is crucial for optimizing pricing strategies and resource allocation within the hospitality industry. Current research focuses on developing sophisticated forecasting models, employing techniques like neural networks (including variations such as hybrid models and deep learning architectures like CNNs with attention mechanisms), to capture complex temporal patterns and the influence of various factors (e.g., price elasticity, seasonality, external events like pandemics). These improved forecasting capabilities offer significant benefits for hotel management, enabling more effective revenue management and sustainable tourism planning.