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.
In 3.4 and 3.5 you said that you are extracting features from Fourier Transforms and ARIMA and saving those features to pandas dataframe. Can you please explain to me how exactly I can extract those components and save them to the dataframe?
In 3.4 and 3.5 you said that you are extracting features from Fourier Transforms and ARIMA and saving those features to pandas dataframe. Can you please explain to me how exactly I can extract those components and save them to the dataframe?