<![CDATA[This study compares machine learning and econometric approaches for forecasting agricultural export values in volatile global markets, examining predictive accuracy and economic interpretability trade-offs. Monthly data from January 2014 to December 2023 were analyzed using five models: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Hybrid ANN-LSTM, Ordinary Least Squares (OLS), and Autoregressive Distributed Lag (ARDL). Key predictors included durian, mangosteen, and longan export values/volumes, plus China's GDP. Performance evaluation used MAE, RMSE, MAPE, and R² metrics with systematic hyperparameter optimization through grid search and 5-fold cross-validation. ANN achieved the highest absolute accuracy (MAE: 1,684,667,401.55; RMSE: 2,602,671,952.28), while Hybrid ANN-LSTM delivered superior relative accuracy (MAPE: 1.58%). ARDL demonstrated exceptional explanatory power (R²=0.83) for structural economic relationships. China's GDP emerged as the strongest determinant across all models. Longan export value showed contrasting effects between approaches, positive in machine learning models versus negative in econometric models, reflecting different paradigmatic interpretations of market substitution dynamics. This research introduces the first comprehensive comparative framework integrating advanced hybrid neural networks with traditional econometric methods for multi-commodity agricultural forecasting, addressing cross-commodity substitution effects previously unexplored while offering complementary perspectives for both predictive accuracy and economic policy interpretation.]]>
