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borisbanushev / stockpredictionai

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.
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please do check if you have used future information for price prediction!!! #114

Open Foristkirito opened 4 years ago

lizhoj commented 4 years ago

I have similar concern.

stevexxs commented 4 years ago

@borisbanushev It seems when using ARIMA, Fourier transforms, VAE, there is possible that use future information, could you please tell me how you tried to avoid this ? Thanks !

lizhoj commented 4 years ago

I don't consider iFFT as a suitable way for prediction. And I wonder how it helps here?

FelixKleineBoesing commented 4 years ago

My guess is, that he decomposes the whole time series with fft and arima and used the very same features as variables in the GAN/Xgboost.

IISuperluminaLII commented 4 years ago

should be alright, as long as you are not underfitting on anything. Everything up-until the GAN is just the "features" part of the dataset that the GAN trains on. Think of it like a very black-boxy-hypercomplex AIRMA. As long as you are cleaning up your data for extremes it should work about like "fine" as far as I can see. I think there is some substance here, just needs more ML research to robustifiy it for better hidden layer regressions (I think that's why he added bayesian opts).