GitHub - NatalieCao323/TimeGAN: Our primary objective is to create complex, multi-dimensional stock return data from sampled Gaussian noise. The project utilizes 1-D convolutional neural networks (CNNs) within the GAN architecture for both the generator and discriminator components.

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
DataProcessing.py		DataProcessing.py
TimeGAN.py		TimeGAN.py
autocorrelation.png		autocorrelation.png
stock_returns.csv.zip		stock_returns.csv.zip

About

Our primary objective is to create complex, multi-dimensional stock return data from sampled Gaussian noise. The project utilizes 1-D convolutional neural networks (CNNs) within the GAN architecture for both the generator and discriminator components.