In this report we try to conduct a classical statistical analysis of fi- nancial time-series to 1) study the Goldman Sachs closing price using ARIMA/GARCH processes, and 2) leverage these methods to better the prediction quality of several machine and deep learning models, mainly recurrent neural networks (RNN). We also discuss the possibility of using ARIMA/GARCH and Fourier analysis as features to generate realistic financial time-series through a Generative Adversarial Network   ar- chitecture inspired by . The code and the data are available in .
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