Vine Copula Based Data Generation for Machine Learning with an Application to Industrial Processes
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.
Cite
Text
Sexton et al. "Vine Copula Based Data Generation for Machine Learning with an Application to Industrial Processes." NeurIPS 2022 Workshops: SyntheticData4ML, 2022.Markdown
[Sexton et al. "Vine Copula Based Data Generation for Machine Learning with an Application to Industrial Processes." NeurIPS 2022 Workshops: SyntheticData4ML, 2022.](https://mlanthology.org/neuripsw/2022/sexton2022neuripsw-vine/)BibTeX
@inproceedings{sexton2022neuripsw-vine,
title = {{Vine Copula Based Data Generation for Machine Learning with an Application to Industrial Processes}},
author = {Sexton, Jean-Thomas and Morin, Michael and Gaudreault, Jonathan},
booktitle = {NeurIPS 2022 Workshops: SyntheticData4ML},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/sexton2022neuripsw-vine/}
}