Generative Models for Stochastic Processes Using Convolutional Neural Networks

Autor:	Neto, Fernando Fernandes
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Computer Science - Neural and Evolutionary Computing Physics - Computational Physics Quantitative Finance - Computational Finance
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1801.03523 Zobrazit plný text záznamu View this record from Arxiv