bstract
International commodity prices are determined by supply and demand, and to a large extent, governmental
interventions through trade barriers and subsidies. Forecasting rice prices has always been a great challenge to researchers
because determinants of supply and demand such as agricultural and environmental factors, meteorological factors,
biophysical factors, changing demographics, etc. interact in a complex manner. Among statistical techniques used to predict
rice prices, researchers have found the Box-Jenkins method to perform well in predicting agricultural farm prices. To study
the underlying forces and structure that produced the observed time series data on Thailand’s weekly rice export prices, we
first used the Box-Jenkins method to fit the data. Next, we evaluated various aggregate measures of forecast error (the mean
absolute deviation, the mean squared error, the mean absolute forecast error, and the root-mean squared error) to assess the
performance of the Box-Jenkins models. Then we used the same data to train and cross-validate artificial neural networks.
Our findings showed that while both Box-Jenkins and artificial neural networks performed well in forecasting the weekly
export prices of Thai rice, the artificial neural networks produce better predictive accuracies in three of the four categories
of rice analyzed.