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.

本研究解决了现有时间序列生成模型在处理短期和长期序列时的局限性。我们提出将序列转换为图像的方法，使得可以使用先进的扩散视觉模型，并在同一框架中有效处理不同长度的输入。实验结果显示，我们的方法在各项任务中均达到了优于先前模型的状态-of-the-art水平，特别是在无条件生成任务中，有显著的性能提升。

利用图像变换和扩散模型进行短期和长期时间序列的生成建模