Vision-Enhanced Time Series Forecasting via Latent Diffusion Models

• URL: http://arxiv.org/abs/2502.14887v1
• Date: Sun, 16 Feb 2025 14:15:06 GMT
• Title: Vision-Enhanced Time Series Forecasting via Latent Diffusion Models
• Authors: Weilin Ruan, Siru Zhong, Haomin Wen, Yuxuan Liang,
• Abstract summary: LDM4TS is a novel framework that leverages the powerful image reconstruction capabilities of latent diffusion models for vision-enhanced time series forecasting.<n>We are the first to use complementary transformation techniques to convert time series into multi-view visual representations.
• Score: 12.54316645614762
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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