SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources

• URL: http://arxiv.org/abs/2009.09471v1
• Date: Sun, 20 Sep 2020 16:36:25 GMT
• Title: SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources
• Authors: Zheng Li, Yue Zhao, Jialin Fu
• Abstract summary: We study synthetic data generation task called downscaling. We propose a multi-stage framework called SYNC (Synthetic Data Generation via Gaussian Copula) We make four key contributions in this work.
• Score: 8.350531869939351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A synthetic dataset is a data object that is generated programmatically, and it may be valuable to creating a single dataset from multiple sources when direct collection is difficult or costly. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this paper, we study a specific synthetic data generation task called downscaling, a procedure to infer high-resolution, harder-to-collect information (e.g., individual level records) from many low-resolution, easy-to-collect sources, and propose a multi-stage framework called SYNC (Synthetic Data Generation via Gaussian Copula). For given low-resolution datasets, the central idea of SYNC is to fit Gaussian copula models to each of the low-resolution datasets in order to correctly capture dependencies and marginal distributions, and then sample from the fitted models to obtain the desired high-resolution subsets. Predictive models are then used to merge sampled subsets into one, and finally, sampled datasets are scaled according to low-resolution marginal constraints. We make four key contributions in this work: 1) propose a novel framework for generating individual level data from aggregated data sources by combining state-of-the-art machine learning and statistical techniques, 2) perform simulation studies to validate SYNC's performance as a synthetic data generation algorithm, 3) demonstrate its value as a feature engineering tool, as well as an alternative to data collection in situations where gathering is difficult through two real-world datasets, 4) release an easy-to-use framework implementation for reproducibility and scalability at the production level that easily incorporates new data.

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