Workshop: Synthetic Data for Empowering ML Research

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

Jean-Thomas Sexton · Michael Morin · Jonathan Gaudreault

[ Abstract ] [ Website ]
[ OpenReview
Fri 2 Dec 7:50 a.m. PST — 7:52 a.m. PST


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.

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