Vine Copula Based Data Generation for Machine Learning With an Application to Industrial Processes

Jean-Thomas Sexton, Michael Morin, Jonathan Gaudreault

Keywords: Synthetic Data, Vine Copulas, Industry 4.0

TL;DR: We use knowledge from experts and data to make Vine Copulas that will generate synthetic data in a data poor environment

Abstract: Synthetic data generation of industrial processes exhibiting non-stationarity and complex, non-linear dependencies between their inputs and outputs is a challenging task. We argue that vine copula models are particularly well suited for this problem and present a method combining limited available data and expert knowledge in order to generate synthetic data by conditionally sampling from a C-Vine, a type of vine copula. We demonstrate our approach by generating synthetic data for a high speed, sophisticated lumber finishing machine called a wood planer.