Mixed-Frequency Model Forecasting of Coastal Tourism Demand Based on Network Index

A novel approach using mixed-frequency modeling demonstrates significant improvements in forecasting coastal tourism demand, particularly for UNESCO World Heritage Sites. The study leverages high-frequency Baidu Index data and low-frequency tourist growth rates to outperform traditional models like the ADL baseline. By employing a MIDAS model combined with XGBoost machine learning, researchers found that sudden health events notably impact predictive accuracy. This method offers valuable insights for sustainable tourism planning, balancing both academic rigor and practical applications.

(Ed.). (2025). Mixed-Frequency Model Forecasting of Coastal Tourism Demand Based on Network Index. Academic Journal of Business & Management, 7(11). https://doi.org/10.25236/ajbm.2025.071108