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

Author

Listed:
  • 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
    Faculty of Environment and Labour Safety, 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
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • 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)

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.

Suggested Citation

  • Saeed Nosratabadi & Amirhosein Mosavi & Puhong Duan & Pedram Ghamisi & Ferdinand Filip & Shahab S. Band & Uwe Reuter & Joao Gama & Amir H. Gandomi, 2020. "Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods," Mathematics, MDPI, vol. 8(10), pages 1-25, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1799-:d:428986
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    References listed on IDEAS

    as
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    3. David G. Green, 2023. "Emergence in complex networks of simple agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 419-462, July.
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    5. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
    6. Lin, Yong & Wang, Renyu & Gong, Xingyue & Jia, Guozhu, 2022. "Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

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