Stock price prediction using Generative Adversarial Networks

1 code implementation • Journal of Computer Science 2021 • HungChun Lin, Chen Chen, Gaofeng Huang, Amir Jafari

In this paper, it proposes a stock prediction model using Generative Adversarial Network (GAN) with Gated Recurrent Units (GRU) used as a generator that inputs historical stock price and generates future stock price and Convolutional Neural Network (CNN) as a discriminator to discriminate between the real stock price and generated stock price.

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