What question did this study set out to answer?

The research aims to develop a nowcasting system for Ecuador's international tourist arrivals to facilitate timely tourism management decisions.

April 22, 2026Open Access

Open-Data Nowcasting of Ecuador’s International Tourist Arrivals: Regularized Dynamic Regression with Wikipedia Attention and Copernicus Land Reanalysis Climate Signals

Puntos clave

The research aims to develop a nowcasting system for Ecuador's international tourist arrivals to facilitate timely tourism management decisions.
Developed a reproducible nowcasting pipeline using Python.
Integrated official arrival data, Wikipedia pageviews, and climate variables from ERA5-Land.
Evaluated multiple forecasting models with statistical metrics for accuracy.
Ridge-regularized autoregressive model achieved a 79% reduction in RMSE compared to the seasonal naïve baseline.
Consistent forecasting accuracy was noted during stress-test periods (2019-2021) and improved post-COVID (2022-2025).
The approach highlighted the variable benefit of exogenous signals across different time windows.

Resumen

Timely monitoring of tourism demand is essential for destination management, yet official monthly arrival statistics are often released with delays and can be difficult to use for near-real-time decision-making, particularly under structural shocks such as coronavirus disease 2019 (COVID-19). This study develops a fully reproducible, open-data nowcasting pipeline for Ecuador’s international tourist arrivals using a Python workflow. The framework integrates (i) the official monthly arrivals series published by Ecuador’s Ministry of Tourism (MINTUR), (ii) open online attention proxies from Wikipedia pageviews retrieved via the Wikimedia REST application programming interface (API), and (iii) open climate covariates derived from the ERA5-Land land reanalysis. Multiple forecasting models are evaluated under a rolling-origin, one-step-ahead backtest, with a mandatory seasonal naïve benchmark and a regime-aware assessment that separates a stress-test window (2019–2021) from an operational post-COVID window (2022–2025). Forecast accuracy is summarized using root mean squared error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE), and statistical significance of performance differences is assessed using the Diebold–Mariano (DM) test. Results show that a ridge-regularized autoregressive model (ridgeₐr) achieves the best overall accuracy, reducing RMSE by approximately 79% relative to the seasonal naïve baseline over the full evaluation window. Windowed results confirm robust performance during the shock period and sustained improvements in the post-2022 operational regime, while the incremental benefit of broader exogenous signals is heterogeneous across windows, underscoring the importance of regularization and regime-aware reporting. The proposed approach provides a transparent, low-cost blueprint for reproducible tourism monitoring that is transferable to other destinations using open data and standard computational tools.

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