Generating Stochastic Processes Through Convolutional Neural Networks

Abstract

The present work establishes the use of convolutional neural networks as a generative model for stochastic processes that are widely present in industrial automation and system modelling such as fault detection, computer vision and sensor data analysis. This enables researchers from a broad range of fields—as in medical imaging, robotics and control engineering—to develop a general tool for artificial data generation and simulation without the need to identify or assume a specific system structure or estimate its parameters. We demonstrate the approach as a generative model on top of data retrieved from a wide set of classic, simplest to the most complex, deterministic and stochastic data generation processes of technological importance—from damped oscillators to autoregressive conditional heteroskedastic and jump-diffusion models. Also, a nonparametric estimation and forecast was carried out for the traditional benchmark “Fisher River” time-series dataset, yielding the superior mean absolute prediction error results compared to a standard ARIMA model. This approach can have potential applications as an alternative to simulation tools such as Gibbs sampling and Monte Carlo-based methods, in the enhancement of the understanding of generative adversarial networks (GANs) and in data simulation for training Reinforcement Learning algorithms.