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arXiv · 2203.17250

Generation and Simulation of Synthetic Datasets with Copulas

Regis Houssou·Mihai-Cezar Augustin·Efstratios Rappos·Vivien Bonvin·Stephan Robert-Nicoud
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Abstract

This paper proposes a new method to generate synthetic data sets based on copula models. Our goal is to produce surrogate data resembling real data in terms of marginal and joint distributions. We present a complete and reliable algorithm for generating a synthetic data set comprising numeric or categorical variables. Applying our methodology to two datasets shows better performance compared to other methods such as SMOTE and autoencoders.

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Regis Houssou, Mihai-Cezar Augustin, Efstratios Rappos, Vivien Bonvin, Stephan Robert-Nicoud. 2022-03-30. Generation and Simulation of Synthetic Datasets with Copulas. https://arxiv.org/abs/2203.17250

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