Ilan Naiman*, Nimrod Berman*, Itai Pemper, Idan Arbiv, Gal Fadlon, and Omri Azencot. * joint first authors NeurIPS, 2024. Abstract Lately, there has been a surge in interest surrounding generative modeling of time series data. Most existing approaches are designed either to process short sequences or to handle long-range sequences. This dichotomy can be attributed to gradient issues with recurrent networks, computational costs associated with transformers, and limited expressiveness of state space models. Towards a unified generative model for varying-length time series, we propose in this work to transform sequences into images. By employing invertible transforms such as the delay…