  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.
