In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
I am learning how to use Deep Learning, GAN and RL to do time series forecasting, and came across your marvelous project. The ideas on your notebook are immediately attractive to me, and really appreciate you putting out such an amazing effort. Especially the way to combined RL and GAN was really interesting. I'd love to drill down to learn more how RL and GAN models are built, and how features data is prepared to train the respective models, etc. If it is possible, I'd like to ask you the favor to share your code for further studying and learning.
Hi Boris,
I am learning how to use Deep Learning, GAN and RL to do time series forecasting, and came across your marvelous project. The ideas on your notebook are immediately attractive to me, and really appreciate you putting out such an amazing effort. Especially the way to combined RL and GAN was really interesting. I'd love to drill down to learn more how RL and GAN models are built, and how features data is prepared to train the respective models, etc. If it is possible, I'd like to ask you the favor to share your code for further studying and learning.
Thanks in advance.