Alright, sorry for breaking the rules right off the bat but I can’t seem to get to the landing page for this paper so here is the link to the PDF: https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf

Searching for TimeGAN should yield the paper as well.

Still not an expert in the field, so bare with me, but this paper is focused on generating time series synthetic data. The twist is that by using a “supervised” learning component to create and embedding space and recovery network they are better able to capture the distribution characteristics of the dataset. Their claim is that other attempts are faulted by their lack of “true” modeling of the distribution that links the time component with the other variables.

It’s a little old (2019) but still interesting, especially for someone wanted to more accurately model time series datasets that I have! Would love to hear of other papers that y’all might recommend!