Generative Models for Stochastic Processes Using Convolutional Neural Networks
Machine Learning
2018-01-12 v1 Neural and Evolutionary Computing
Computational Physics
Computational Finance
Abstract
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.
Cite
@article{arxiv.1801.03523,
title = {Generative Models for Stochastic Processes Using Convolutional Neural Networks},
author = {Fernando Fernandes Neto},
journal= {arXiv preprint arXiv:1801.03523},
year = {2018}
}