Stock price prediction using Generative Adversarial Networks

Article Properties
Cite
Lin, HungChun, et al. “Stock Price Prediction Using Generative Adversarial Networks”. Journal of Computer Science, vol. 17, no. 3, 2021, pp. 188-96, https://doi.org/10.3844/jcssp.2021.188.196.
Lin, H., Chen, C., Huang, G., & Jafari, A. (2021). Stock price prediction using Generative Adversarial Networks. Journal of Computer Science, 17(3), 188-196. https://doi.org/10.3844/jcssp.2021.188.196
Lin H, Chen C, Huang G, Jafari A. Stock price prediction using Generative Adversarial Networks. Journal of Computer Science. 2021;17(3):188-96.
Citations
Title Journal Journal Categories Citations Publication Date
Multi-factor stock price prediction based on GAN-TrellisNet Knowledge and Information Systems
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Technology (General): Industrial engineering. Management engineering: Information technology
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 1 2024
Stock market forecasting using DRAGAN and feature matching Expert Systems with Applications
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Manufactures: Production management. Operations management
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 2024
Carbon Price Prediction for the European Carbon Market Using Generative Adversarial Networks Modern Economy 2024
An enhanced Wasserstein generative adversarial network with Gramian Angular Fields for efficient stock market prediction during market crash periods Applied Intelligence
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 1 2023
A deep comprehensive model for stock price prediction Journal of Ambient Intelligence and Humanized Computing
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Science: Science (General): Cybernetics: Information theory
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 2023
Citations Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science7
Science: Science (General): Cybernetics: Information theory5
Technology: Mechanical engineering and machinery2
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics2
Technology: Engineering (General). Civil engineering (General)2
Technology: Technology (General): Industrial engineering. Management engineering: Information technology2
Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication2
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks1
Technology: Manufactures: Production management. Operations management1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware1
Technology: Technology (General): Industrial engineering. Management engineering: Management information systems1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 7 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Knowledge graph and deep learning combined with a stock price prediction network focusing on related stocks and mutation points and was published in 2022. The most recent citation comes from a 2024 study titled Multi-factor stock price prediction based on GAN-TrellisNet. This article reached its peak citation in 2023, with 4 citations. It has been cited in 11 different journals, 36% of which are open access. Among related journals, the Knowledge and Information Systems cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year