Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Version 1 : Received: 11 October 2020 / Approved: 13 October 2020 / Online: 13 October 2020 (09:24:17 CEST)

A peer-reviewed article of this Preprint also exists.

Nosratabadi, S.; Mosavi, A.; Duan, P.; Ghamisi, P.; Filip, F.; Band, S.S.; Reuter, U.; Gama, J.; Gandomi, A.H. Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods. Mathematics 2020, 8, 1799. Nosratabadi, S.; Mosavi, A.; Duan, P.; Ghamisi, P.; Filip, F.; Band, S.S.; Reuter, U.; Gama, J.; Gandomi, A.H. Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods. Mathematics 2020, 8, 1799.

Journal reference: Mathematics 2020, 8, 1799
DOI: 10.3390/math8101799

Abstract

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.

Subject Areas

data science; deep learning; ensemble machine learning models; economics; hybrid models

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