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.
Hi I am a student studying AI as it relates to economics and finance at Northwestern and came across your work on Medium. This approach to using GANs in a forecasting setting is fascinating to me. I would love to play around with your code, if possible, to better learn you implementation method. Thanks!
Hi I am a student studying AI as it relates to economics and finance at Northwestern and came across your work on Medium. This approach to using GANs in a forecasting setting is fascinating to me. I would love to play around with your code, if possible, to better learn you implementation method. Thanks!