In Stock Market Prediction Based on Generative Adversarial Network by K. Zhang et. al, the authors feed financial data (X0...Xt) into an LSTM to predict Xt+1.

Then, they evaluate whether the series (X0...Xt+1) is real or not (with as Xt+1 either the predicted or the one appearing in the data).

This is done in a GAN system, with the predictor trained to create an Xt+1 so that the series would fool the discriminator.

What is the benefit of training the predictor as a generator in GAN, with the discriminator in providing the loss function, when it would be simpler to use a loss based on |predXt+1 - Xt+1| ?

After all, the aim is to predict the target values - so why not train using the fit of the prediction directly?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.