Forecasting tourist flows in Peru's major destinations to support sustainable management through data-driven approaches

Pinedo L.; Solano-Lavado M.-S.; Scattolon-Huapaya L.; Fernández-Trujillo M.-D.-C.; Umaña-Chacón F. · Frontiers in Sustainable Tourism (2026) · DOI: 10.3389/frsut.2026.1876343

Resumen

Forecasting tourist flows is essential for strengthening the sustainable management of destinations, especially in post-pandemic contexts characterized by high seasonality and uncertainty. This study projects tourist flows in Peru's main destinations using a data-driven framework integrating statistical models and machine learning techniques. A monthly database was constructed incorporating temporal, seasonal, and climatic variables, as well as events and digital behavior, which were evaluated using six models (Seasonal Naïve, SARIMA, SARIMAX, Random Forest, XGBoost, and LightGBM). The results show that the Seasonal Naïve model achieves the highest predictive accuracy (MAPE = 18.49% for national tourism and 12.06% for international tourism), confirming the predominance of seasonal patterns in the destinations analyzed. However, machine learning models offer greater explanatory power (R2 = 0.923), capturing non-linear relationships in tourism demand. Projections for 2026 show a sustained recovery in international tourism and peaks in demand between May and August, with levels exceeding 130, 000 monthly visitors in key destinations. The results demonstrate that data-driven forecasting enables the anticipation of demand scenarios and supports decisions aimed at sustainable tourism management, optimizing operational capacity and reducing the risk of overcrowding. Copyright

Instituciones: Universidad Privada Norbert Wiener