基于机器学习的主要汽车生产国外汇交易数据研究

白玉培　章芷洋　唐艇宗　张艾嘉　李琳玉

摘 要：21世紀以来，AI人工智能以及计算机技术、通讯技术的快速发展，人工智能的核心技术机器学习逐渐成为研究的热点。在当前这个大数据的时代，机器学习的应用前景十分广阔，更多的行业选择使用机器学习进行数据的处理。汽车工业在发达及发展中国家中的经济占比中较重，主要汽车生产国的外汇交易数据在商业实践与国际贸易中具有重要意义。同时，金融交易领域存在大量的历史数据，而这些数据资源为预测外汇价格以及跌涨趋势具有重要的作用。文章针对主要汽车生产国外汇交易数据进行了预处理和特征转换，并对比分析了支持向量机、随机森林、以及XGBoost模型对外汇交易数据评估的预测能力。研究结果表明XGBoost要优于传统的支持向量机和随机森林。

关键词：外汇交易 支持向量机 随机森林 XGBoost

Research on Foreign Exchange Data of Main Automobile Production based on Machine Learning

Bai Yupei Zhang Zhiyang Tang Tingzong Zhang Aijia Li Linyu

Abstract：Since the 21st century， with the rapid development of AI， computer technology and communication technology， machine learning， the core technology of AI， have gradually become research hotspots. In the current era of big data， machine learning has a very broad application prospect. More industries choose to use machine learning for data processing. The automobile industry accounts for a large proportion of the economy in developed and developing countries. The foreign exchange data of major automobile producing countries are of great significance in business practice and international trade. At the same time， there are a lot of historical data in the field of financial transactions， and these data resources play an important role in predicting foreign exchange prices and the trend of decline and rise. This paper preprocesses and transforms the main foreign exchange trade data of automobile production， and analyzes the prediction ability of support vector machine， random forest and XGBoost model. The results show that XGBoost is better than traditional support vector machine and random forest.

Key words：foreign exchange trading， support vector machine， random forest， XGBoost