Forecasting daily visits in Shanghai with model combination and Telco big data

Analyzing and forecasting visitor flows has significant potential to enhance policy decisions, facilitate long- and short-term planning of local resources, and optimize tourism offerings. Leveraging Big Data in this context provides distinct advantages, particularly regarding real-time predictions. This paper proposes an econometric approach to account for uncertainty in the prediction model, a key source of forecasting errors. A description and prediction of daily visits in Shanghai City are provided using models commonly employed in tourist flows analysis. We show that model combination and selection procedures can serve as valuable tools for policymakers, providing accurate forecasts to support tourist flow management.