Generative Models for Stochastic Processes Using Convolutional Neural Networks. (arXiv:1801.03523v1 [stat.ML])

The present paper aims to demonstrate the usage of Convolutional Neural Networks as a generative model for stochastic processes, enabling researchers from a wide range of fields (such as quantitative finance and physics) to develop a general tool for forecasts and simulations without the need to identify/assume a specific system structure or estimate its parameters.

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