# Sequential Models in the Synthetic Data Vault

arXiv: https://arxiv.org/abs/2207.14406 · HTML: https://mlsanity.com/paper/2207.14406/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)

## Built on by (most cited first, 12 of 12)

- [Transforming Wearable Data into Personal Health Insights using Large Language Model Agents](https://mlsanity.com/paper/2406.06464/) (2024)
- [TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series](https://mlsanity.com/paper/2305.11567/) (2023)
- [Federated Learning for 5G Base Station Traffic Forecasting](https://mlsanity.com/paper/2211.15220/) (2022)
- [Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations](https://mlsanity.com/paper/2310.01684/) (2023)
- [Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective](https://mlsanity.com/paper/2401.00965/) (2024)
- [TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data](https://mlsanity.com/paper/2406.16028/) (2024)
- [Adversarial Causal Tuning for Realistic Time-series Generation](https://mlsanity.com/paper/2506.02084/) (2025)
- [FUELVISION: A Multimodal Data Fusion and Multimodel Ensemble Algorithm for Wildfire Fuels Mapping](https://mlsanity.com/paper/2403.15462/) (2024)
- [TrialSynth: Generation of Synthetic Sequential Clinical Trial Data](https://mlsanity.com/paper/2409.07089/) (2024)
- [Generation of synthetic gait data: application to multiple sclerosis patients' gait patterns](https://mlsanity.com/paper/2411.10377/) (2024)
- [Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models](https://mlsanity.com/paper/2604.21031/) (2026)
- [Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data](https://mlsanity.com/paper/2607.15606/) (2026)

## Abstract (arXiv)

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.