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In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.
Journal: Journal of Computer Science
Title: Stock price prediction using Generative Adversarial Networks
https://thescipub.com/abstract/jcssp.2021.188.196
DATS6501 Capstone Team member: Chen Chen, HungChun Lin
In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.
The weekly meeting notes are also the progress of our project went through by week.
This file includes the articles/GitHub we have referenced to complete this project.
The final report for our project in pdf.
The group presentation slides in pdf.
The code used for data preprocessing and modeling.
Please check code folder for detailed instruction.
There are also pretained models that can generate stock price.