A Machine Learning Approach to Forecast International Trade: The Case of Croatia

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-10-01 DOI:10.2478/bsrj-2022-0030

Hrvoje Jošić, Berislav Žmuk

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引用次数: 2

Abstract

Abstract Background: This paper presents a machine learning approach to forecast Croatia’s international bilateral trade. Objectives: The goal of this paper is to evaluate the performance of machine learning algorithms in predicting international bilateral trade flows related to imports and exports in the case of Croatia. Methods/Approach: The dataset on Croatian bilateral trade with over 180 countries worldwide from 2001 to 2019 is assembled using main variables from the gravity trade model. To forecast values of Croatian bilateral exports and imports for a horizon of one year (the year 2020), machine learning algorithms (Gaussian processes, Linear regression, and Multilayer perceptron) have been used. Each forecasting algorithm is evaluated by calculating mean absolute percentage errors (MAPE). Results: It was found that machine learning algorithms have a very good predicting ability in forecasting Croatian bilateral trade, with neural network Multilayer perceptron having the best performance among the other machine learning algorithms. Conclusions Main findings from this paper can be important for economic policymakers and other subjects in this field of research. Timely information about the changes in trends and projections of future trade flows can significantly affect decision-making related to international bilateral trade flows.

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预测国际贸易的机器学习方法:克罗地亚的案例

摘要背景:本文提出了一种机器学习方法来预测克罗地亚的国际双边贸易。目的:本文的目标是评估机器学习算法在预测克罗地亚情况下与进出口有关的国际双边贸易流量方面的性能。方法/方法:利用重力贸易模型中的主要变量，对2001年至2019年克罗地亚与全球180多个国家的双边贸易数据集进行了汇总。为了预测克罗地亚一年(2020年)的双边进出口价值，使用了机器学习算法(高斯过程、线性回归和多层感知器)。通过计算平均绝对百分比误差(MAPE)来评估每种预测算法。结果:发现机器学习算法在预测克罗地亚双边贸易方面具有很好的预测能力，其中神经网络多层感知器在其他机器学习算法中表现最好。本文的主要发现对经济政策制定者和该领域的其他研究对象具有重要意义。关于未来贸易流量趋势变化和预测的及时信息可以对与国际双边贸易流量有关的决策产生重大影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.