Vision-Enhanced Time Series Forecasting via Latent Diffusion Models

TL;DR

This paper introduces LDM4TS, a novel framework that transforms time series data into visual representations to leverage latent diffusion models for improved forecasting accuracy.

Contribution

It is the first to use visual transformation techniques with latent diffusion models for time series forecasting, enhancing feature extraction and prediction performance.

Findings

LDM4TS outperforms existing forecasting models.

Visual representations improve temporal pattern capture.

The framework effectively exploits pre-trained vision encoders.

Abstract

Diffusion models have recently emerged as powerful frameworks for generating high-quality images. While recent studies have explored their application to time series forecasting, these approaches face significant challenges in cross-modal modeling and transforming visual information effectively to capture temporal patterns. In this paper, we propose LDM4TS, a novel framework that leverages the powerful image reconstruction capabilities of latent diffusion models for vision-enhanced time series forecasting. Instead of introducing external visual data, we are the first to use complementary transformation techniques to convert time series into multi-view visual representations, allowing the model to exploit the rich feature extraction capabilities of the pre-trained vision encoder. Subsequently, these representations are reconstructed using a latent diffusion model with a cross-modal conditioning mechanism as well as a fusion module. Experimental results demonstrate that LDM4TS outperforms various specialized forecasting models for time series forecasting tasks.