Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series

• URL: http://arxiv.org/abs/2410.19538v1
• Date: Fri, 25 Oct 2024 13:06:18 GMT
• Title: Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series
• Authors: Ilan Naiman, Nimrod Berman, Itai Pemper, Idan Arbiv, Gal Fadlon, Omri Azencot,
• Abstract summary: We propose a unified generative model for varying-length time series. We employ invertible transforms such as the delay embedding and the short-time Fourier transform. We show that our approach achieves consistently state-of-the-art results against strong baselines.
• Score: 7.201938834736084
• License:
• 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 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.

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