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International agricultural trade forecasting using machine learning
2021
Data & Policy
Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective
doi:10.1017/dap.2020.22
fatcat:7kxl6sfl2nh4naapq72wrcrgqa