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arXiv 2003.13422q-fin.GNcs.LGstat.ML
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Data Science in Economics

School of the Built Environment, Oxford Brookes University（牛津布鲁克斯大学建筑环境学院）；Faculty of Civil Engineering, Technische Universität Dresden（德累斯顿工业大学土木工程学院）；College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心弗莱贝格资源技术亥姆霍兹研究所）

Saeed Nosratabadi, Amir Mosavi, Puhong Duan, Pedram Ghamisi

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22pages, 4 figures, 9 tables
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英文摘要：

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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