Generative Models for Stochastic Processes Using Convolutional Neural Networks
This provides a general tool for researchers in fields like quantitative finance and physics to generate stochastic processes without structural assumptions.
The paper tackles the problem of generating stochastic processes for forecasts and simulations by using Convolutional Neural Networks as a generative model, enabling general tool development without requiring specific system identification or parameter estimation.
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.