Generation and Simulation of Synthetic Datasets with Copulas

• URL: http://arxiv.org/abs/2203.17250v1
• Date: Wed, 30 Mar 2022 13:22:44 GMT
• Title: Generation and Simulation of Synthetic Datasets with Copulas
• Authors: Regis Houssou, Mihai-Cezar Augustin, Efstratios Rappos, Vivien Bonvin and Stephan Robert-Nicoud
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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