Sequential Models in the Synthetic Data Vault

• URL: http://arxiv.org/abs/2207.14406v1
• Date: Thu, 28 Jul 2022 23:17:51 GMT
• Title: Sequential Models in the Synthetic Data Vault
• Authors: Kevin Zhang, Neha Patki, Kalyan Veeramachaneni
• Abstract summary: The goal of this paper is to describe a system for generating synthetic sequential data within the Synthetic data vault. We present the Sequential model currently in SDV, an end-to-end framework that builds a generative model for multi-sequence, real-world data.
• Score: 8.35780131268962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of this paper is to describe a system for generating synthetic sequential data within the Synthetic data vault. To achieve this, we present the Sequential model currently in SDV, an end-to-end framework that builds a generative model for multi-sequence, real-world data. This includes a novel neural network-based machine learning model, conditional probabilistic auto-regressive (CPAR) model. The overall system and the model is available in the open source Synthetic Data Vault (SDV) library {https://github.com/sdv-dev/SDV}, along with a variety of other models for different synthetic data needs. After building the Sequential SDV, we used it to generate synthetic data and compared its quality against an existing, non-sequential generative adversarial network based model called CTGAN. To compare the sequential synthetic data against its real counterpart, we invented a new metric called Multi-Sequence Aggregate Similarity (MSAS). We used it to conclude that our Sequential SDV model learns higher level patterns than non-sequential models without any trade-offs in synthetic data quality.

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