Forecasting Peruvian Blueberry Exports employing Markov Chains, SARIMA, and Log-Linear Growth

This study analyzes the export dynamics of Peruvian fresh blueberries by comparing three forecasting approaches: a Markov–Monte Carlo model, a log-linear growth mod-el, and a seasonal SARIMA model. Monthly data on export value (FOB), export vol-ume, and unit price covering the period 2012–2025 are employed. The analysis com-bines descriptive statistics, seasonal Markov transition matrices, out-of-sample fore-cast evaluation using MAE and RMSE, and residual diagnostics. The results show that log-linear growth models provide accurate short-term point forecasts for export val-ues, reflecting the dominance of a strong trend component. SARIMA models perform better for export volumes and exhibit favorable residual properties, supporting their use for uncertainty quantification. In contrast, the Markov–Monte Carlo approach yields superior performance for export prices and offers valuable insights into seasonal regimes, persistence, and risk through simulated distributions. Overall, the findings indicate that no single model dominates across all dimensions of the export chain. A combined use of deterministic, stochastic, and regime-based approaches provides a more comprehensive framework for forecasting and risk management in dynamic ag-ricultural export sectors.