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

EasyChair Preprint 4392

Abstract

This paper provides a state-of-the-art investigation on advances in data science in emerging economic applications. 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.

Keyphrases: Cryptocurrency, Data Science, Economics, Ensemble Machine Learning Models, Forecasting, Stock value, deep learning, hybrid machine learning, prediction, stock price

@booklet{EasyChair:4392,
  author    = {Saeed Nosratabadi and Amir Mosavi and Puhong Duan and Pedram Ghamisi and Ferdinand Filip and Shahab S. Band and Uwe Reuter and Joao Gama and Amir H. Gandomi},
  title     = {Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods},
  howpublished = {EasyChair Preprint 4392},
  year      = {EasyChair, 2020}}