机器学习在国际贸易预测中的应用 趋势

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摘要翻译：
国际贸易政策最近因限制基本货物（如钢、铝、大豆和牛肉）的跨境交换而受到关注。由于贸易严重影响就业和工资，预测未来的贸易模式是世界各地决策者的高度优先事项。虽然传统的经济模型旨在成为可靠的预测者，但我们考虑机器学习(ML)技术允许更好的预测来为政策决策提供信息的可能性。开放的政府数据为算法提供了动力，这些算法可以解释和预测贸易流动，从而为政策提供信息。本文收集的数据描述了国际贸易交易和通常相关的经济因素。部署的机器学习(ML)模型包括：用于预测未来贸易模式的ARIMA、GBoost、XGBoost、LightGBM，以及根据经济因素对国家进行K-均值聚类。与短期和主观（直线）预测和中期（aggre门控）预测不同，ML方法为单个商品提供了一系列数据驱动和可解释的预测。介绍了模型、它们的结果和策略，并对预测质量进行了评估。
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英文标题：
《Application of Machine Learning in Forecasting International Trade
  Trends》
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作者：
Feras Batarseh, Munisamy Gopinath, Ganesh Nalluru, Jayson Beckman
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/1910.03112

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关键词：国际贸易 机器学习 econometrics Transactions Applications 解释 决策者 patterns predicting future