Forecasting the number of inbound tourists with Google Trends

With the increasing popularity of tourism activities, the forecasting of tourist volume has become an important research issue in the field of tourism management. However, the traditional statistical data cannot reflect the changes in tourism demand in real time. In order to make up for this shortcoming, scholars have found that web search data and big data technologies can provide a new way to forecast tourism demand which can expose user behavioral intentions in real time. Accordingly, this paper tries to make a prediction of the number of China inbound foreign tourists based on Google Trends data, and by applying Random Forest (RF) model to this task, a higher prediction accuracy has been achieved.