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Application of Machine Learning in Forecasting International Trade Trends

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Document pages: 4 pages

Abstract: International trade policies have recently garnered attention for limitingcross-border exchange of essential goods (e.g. steel, aluminum, soybeans, andbeef). Since trade critically affects employment and wages, predicting futurepatterns of trade is a high-priority for policy makers around the world. Whiletraditional economic models aim to be reliable predictors, we consider thepossibility that Machine Learning (ML) techniques allow for better predictionsto inform policy decisions. Open-government data provide the fuel to power thealgorithms that can explain and forecast trade flows to inform policies. Datacollected in this article describe international trade transactions andcommonly associated economic factors. Machine learning (ML) models deployedinclude: ARIMA, GBoosting, XGBoosting, and LightGBM for predicting future tradepatterns, 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 andinterpretable projections for individual commodities. Models, their results,and policies are introduced and evaluated for prediction quality.

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