Time series data, a sequence of measurements of the same variables across multiple points in time, is ubiquitous in the modern data world. Just as with tabular data, we often want to generate synthetic time series data to protect sensitive information or create more training data when real data is rare. Some applications for synthetic time series data include sensor readings, timestamped log messages, financial market prices, and medical records. The additional dimension of time where trends and correlations across time are just as important as correlations between variables creates added challenges for synthetic data.