High-accuracy prediction of international raw material trade flows using temporal fusion transformer

Accurate predictions of international trade flows form the basis for informed decision-making and strategic planning at both national and global levels. By offering reliable forecasts of market trends, these predictions allow policymakers, businesses, and economic institutions to anticipate shifts in supply and demand, adapt to evolving economic conditions, and mitigate potential risks. We applied a temporal fusion transformer-based (TFT) model with improved precision to predict international raw material trade flows. Our goal is to enhance prediction accuracy and robustness by leveraging the strengths of TFT in handling complex time series data, surpassing the performance of conventional machine learning techniques.Using an enriched dataset from the UN Comtrade database, the CEPII Gravity dataset, and the World Bank, our model achieves a 17% increase in R2 compared to baseline models random forest and graph attention networks. Furthermore, the proposed model offers improved interpretability regarding feature importance, providing clearer insights into trade flow predictions.Our analysis demonstrates the TFT model’s ability to cope with economic disruptions such as COVID-19 and the Ukraine conflict, proving its reliability in volatile trade conditions. This work represents the first application of transformer-based methods to multi-horizon forecasting in raw material trade, offering novel insights into global economic trends.