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While most generative models tend to rely on large amounts of training data, here Hans Buehler et al present a generative model that works reliably even in environments where the amount of available training data is small, irregularly paced or oscillatory. They show how a rough paths-based feature map encoded by the signature of the path outperforms returns-based market generation both numerically and from a theoretical point of view. Finally, they propose a
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