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

Saeed Nosratabadi, Amirhosein Mosavi, Puhong Duan, Pedram Ghamisi, Ferdinand Filip, Shahab S. Band, Uwe Reuter, Joao Gama and Amir H. Gandomi
Additional contact information
Saeed Nosratabadi: Doctoral School of Management and Business Administration, Szent Istvan University, 2100 Godollo, Hungary
Amirhosein Mosavi: Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Puhong Duan: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Pedram Ghamisi: Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, D-09599 Freiberg, Germany
Ferdinand Filip: Department of Mathematics, J. Selye University, 94501 Komarno, Slovakia
Shahab S. Band: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Uwe Reuter: Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
Joao Gama: Faculty Laboratory of Artificial Intelligence and Decision Support (LIAAD)-INESC TEC, Campus da FEUP, Rua Roberto Frias, 4200-465 Porto, Portugal
Amir H. Gandomi: Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia

Mathematics, 2020, vol. 8, issue 10, 1-25

Abstract: This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

Keywords: data science; deep learning; economic model; ensemble; economics; cryptocurrency; machine learning; deep reinforcement learning; big data; bitcoin; time series; network science; prediction; survey; artificial intelligence; literature review (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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