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Forecasting the international trade of rice is difficult because demand and supply are affected by many unpredictable factors (e.g., trade barriers and subsidies, agricultural and environmental factors, meteorological factors, biophysical factors, changing demographics, etc.) that interact in a complex manner. This paper compares the performance of artificial neural networks (ANNs) with exponential smoothing and ARIMA models in forecasting rice exports from Thailand. To ascertain that the models can reproduce acceptable results on unseen future, we evaluated various aggregate measures of forecast error (MAE, MSE, MAPE, and RMSE) during the validation process of the models. The results reveal that while the Holt-Winters and the Box-Jenkins models showed satisfactory goodness of fit, the models did not perform as well in predicting unseen data during validation. On the other hand, the ANNs performed relatively well as they were able to track the dynamic non-linear trend and seasonality, and the interactions between them.