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pro vyhledávání: '"Wellman, Micheal P."'
We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design
Externí odkaz:
http://arxiv.org/abs/2006.04212