Is it possible for Google Trends to forecast rural tourism? The situation in Spain

Abstract

The study aims to assess the potential of Google Trends (GT) data for improving the prediction of monthly rural overnight stays by national residents in Spain. The study uses forecasting models that incorporate Google Trends data and compares their accuracy with traditional time-series benchmark models. The data used spans from January 2012 to February 2020. This comparison allows for a direct evaluation of whether GT variables offer any predictive advantage over classical time-series methods in the context of rural tourism. The results indicate that models incorporating GT variables do not outperform traditional time-series models in predicting rural tourism flows in Spain. Previous studies in other tourism sectors have found that GT data enhances predictive accuracy. The study concludes that Google Trends data may have limitations in predicting rural tourism flows, despite its demonstrated utility in other tourism forecasting contexts. These limitations suggest a need for further investigation into why GT does not improve rural tourism forecasts. The findings suggest that tourism industry stakeholders and policymakers should be cautious in relying on GT data for forecasting rural tourism flows.