Public policymaking for international agricultural trade using association rules and ensemble machine learning
| Autor: | Anderson Monken, Zhengrong Gu, Feras A. Batarseh, Munisamy Gopinath |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning General Economics (econ.GN) Association rule learning Computer Science - Artificial Intelligence Computer science Commodity International trade Association rules Black swan theory Machine Learning (cs.LG) FOS: Economics and business Goods and services Black swan events Free trade Economics - General Economics Country–commodity transactions business.industry Affinity analysis QA75.5-76.95 International economics Ensemble learning Gravity models Artificial Intelligence (cs.AI) Agriculture Electronic computers. Computer science Q300-390 business Ensemble machine learning Cybernetics |
| Zdroj: | Machine Learning with Applications, Vol 5, Iss, Pp 100046-(2021) |
| ISSN: | 2666-8270 |
| Popis: | International economics has a long history of improving our understanding of factors causing trade, and the consequences of free flow of goods and services across countries. The recent shocks to the free trade regime, especially trade disputes among major economies, as well as black swan events, such as trade wars and pandemics, raise the need for improved predictions to inform policy decisions. AI methods are allowing economists to solve such prediction problems in new ways. In this manuscript, we present novel methods that predict and associate food and agricultural commodities traded internationally. Association Rules (AR) analysis has been deployed successfully for economic scenarios at the consumer or store level, such as for market basket analysis. In our work however, we present analysis of imports and exports associations and their effects on commodity trade flows. Moreover, Ensemble Machine Learning methods are developed to provide improved agricultural trade predictions, outlier events' implications, and quantitative pointers to policy makers. Comment: Paper published at Elsevier's Journal of Machine Learning with Applications https://www.sciencedirect.com/science/article/pii/S2666827021000232 |
| Databáze: | OpenAIRE |
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