Application of Machine Learning in Forecasting International Trade Trends
This work addresses the need for accurate trade predictions to inform policy decisions, but it appears incremental as it applies existing ML methods to a new dataset in economics.
The paper tackled the problem of forecasting international trade trends by applying machine learning models like ARIMA, GBoosting, XGBoosting, and LightGBM to open-government data, resulting in data-driven and interpretable projections for individual commodities.
International trade policies have recently garnered attention for limiting cross-border exchange of essential goods (e.g. steel, aluminum, soybeans, and beef). Since trade critically affects employment and wages, predicting future patterns of trade is a high-priority for policy makers around the world. While traditional economic models aim to be reliable predictors, we consider the possibility that Machine Learning (ML) techniques allow for better predictions to inform policy decisions. Open-government data provide the fuel to power the algorithms that can explain and forecast trade flows to inform policies. Data collected in this article describe international trade transactions and commonly associated economic factors. Machine learning (ML) models deployed include: ARIMA, GBoosting, XGBoosting, and LightGBM for predicting future trade patterns, and K-Means clustering of countries according to economic factors. Unlike short-term and subjective (straight-line) projections and medium-term (aggre-gated) projections, ML methods provide a range of data-driven and interpretable projections for individual commodities. Models, their results, and policies are introduced and evaluated for prediction quality.