Data science in economics: comprehensive review of advanced machine learning and deep learning methods
Year: 2020
Type: preprint
Study design: Systematic Review
Abstract: This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on 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 wide 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, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.
Institutions: Magyar Agrár- és Élettudományi Egyetem, Bauhaus-Universität Weimar, Norwegian University of Life Sciences, Hunan University, Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, J. Selye University, Duy Tan University, National Yunlin University of Science and Technology, Technische Universität Dresden, INESC TEC, University of Technology Sydney
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4285619938