@inproceedings{
naiman2024utilizing,
title={Utilizing Image Transforms and Diffusion Models for  Generative Modeling of Short and Long Time Series},
author={Ilan Naiman and Nimrod Berman and Itai Pemper and Idan Arbiv and Gal Fadlon and Omri Azencot},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=2NfBBpbN9x}
}
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 embedding and the short-time Fourier transform, we unlock three main advantages: i) We can exploit advanced diffusion vision models; ii) We can remarkably process short- and long-range inputs within the same framework; and iii) We can harness recent and established tools proposed in the time series to image literature. We validate the effectiveness of our method through a comprehensive evaluation across multiple tasks, including unconditional generation, interpolation, and extrapolation. We show that our approach achieves consistently state-of-the-art results against strong baselines. In the unconditional generation tasks, we show remarkable mean improvements of $58.17$% over previous diffusion models in the short discriminative score and $132.61$% in the (ultra-)long classification scores. Code is at https://github.com/azencot-group/ImagenTime.
Utilizing Image Transforms and Diffusion Models for  Generative Modeling of Short and Long Time Series
Ilan Naiman Nimrod Berman Itai Pemper Idan Arbiv Gal Fadlon Omri Azencot