An Euler based GAN for time series

A new model of generative adversarial networks for time series based on Euler scheme and Wasserstein distances including Sinkhorn divergence is proposed . The approach is tested on financial indicators computation on S\&P500 and on an option hedging problem . We show how the proposed methodology can be combined with transfer learning to include the latest historical dataset features . We test our Euler GAN generations with usual Monte Carlo simulations in one-dimension and in a multi-dimensional case. The approach is tested on financial .

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Keywords : euler - approach - time - series - based -

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