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

TL;DR

This paper introduces a unified approach for generating both short and long time series by transforming sequences into images using invertible transforms, enabling the use of diffusion models and achieving state-of-the-art results.

Contribution

The work proposes transforming time series into images with invertible transforms to unify short and long sequence generation using diffusion models.

Findings

Achieves state-of-the-art results in unconditional generation, interpolation, and extrapolation.

Shows significant improvements over previous diffusion models in discriminative and classification scores.

Validates the approach across multiple tasks with comprehensive evaluations.

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.