# Generation and Simulation of Synthetic Datasets with Copulas

arXiv: https://arxiv.org/abs/2203.17250 · HTML: https://mlsanity.com/paper/2203.17250/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)

## Built on by (most cited first, 8 of 8)

- [From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection](https://mlsanity.com/paper/2602.03018/) (2026)
- [MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection](https://mlsanity.com/paper/2602.09329/) (2026)
- [SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems](https://mlsanity.com/paper/2409.12328/) (2024)
- [CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction](https://mlsanity.com/paper/2506.17326/) (2025)
- [The Synthetic Mirror -- Synthetic Data at the Age of Agentic AI](https://mlsanity.com/paper/2506.13818/) (2025)
- [Phase-Type Variational Autoencoders for Heavy-Tailed Data](https://mlsanity.com/paper/2603.01800/) (2026)
- [JANUS: Structured Bidirectional Generation for Guaranteed Constraints and Analytical Uncertainty](https://mlsanity.com/paper/2603.03748/) (2026)
- [From Data to Program: Fast & Direct Generative Program Inference from Empirical Data](https://mlsanity.com/paper/2609.35348/) (2026)

## Abstract (arXiv)

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.