An Analysis of Machine Learning-Based Forecasting of International Trade

全球经济推动着国际贸易的发展。随着全球经济一体化的不断发展，国际贸易预测对政策制定者和企业决策者至关重要。传统计量经济学方法在处理高维、非线性和不断变化的动态数据时会有些吃力。机器学习技术具有强大的数据处理和模式识别能力，给国际贸易预测带来了新的可能性。本文首先综述了相关领域文献，总结出机器学习用于国际贸易的预测的作用和优势，随后回顾了国际贸易预测的传统方法及其局限性，然后详细探讨了机器学习在该领域的应用，并重点分析监督学习、无监督学习和深度学习方法在国际贸易数据建模中的作用。最后得出结论，基于机器学习的预测方法在准确性和适应性方面存在优势，机器学习在国际贸易预测中的应用场景也更为广泛，这为政府和企业制定贸易政策提供了有力支持。The global economy drives the development of international trade. With the continuous development of global economic integration, international trade forecasting is crucial for policymakers and corporate decision makers. Traditional econometric methods have some difficulty in dealing with high-dimensional, nonlinear and constantly changing dynamic data. Machine learning technology has powerful data processing and pattern recognition capabilities, bringing new possibilities to international trade forecasting. This paper first reviews the literature in related fields, summarizes the role and advantages of machine learning in international trade forecasting, reviews the traditional methods of international trade forecasting and their limitations, and then discusses the application of machine learning in this field in detail, and focuses on the role of supervised learning, unsupervised learning and deep learning methods in international trade data modeling. Finally, it is concluded that the forecasting method based on machine learning has advantages in accuracy and adaptability, and the application scenarios of machine learning in international trade forecasting are also more extensive, which provides strong support for governments and enterprises to formulate trade policies.