Forecasting tourism arrivals with an online search engine data: A study of the Balearic Islands

Résumé 0

"This study explores issues related to the forecasting in revenue management in the prediction of tourism arrivals for the Balearic Islands. Specifically, the study uses queries from a web search data (Google Trends) in order to demonstrate the forecasting power of such measures compared to traditional methods. I developed a database formed by the two main tourist volumes and then, I compared each model with its corresponding baseline to figure out whether the Google Trends indicator can increase accuracy of the prediction. Consequently, Granger causality test indicated a positive causality between variables suggesting good estimating results. Besides, I calculated the Mean Absolute Percentage Errors (MAPE) for each model and the results showed a considerable improvement of the Google Trends models compared to baseline models. The results provide some hints for increasing company efficiency and enhance policy maker decision making."