用图像模型提升时间序列预测，通过视觉化转换捕捉时序模式。
Vision-Enhanced Time Series Forecasting via Latent Diffusion Models
- 将时间序列转为多视角图像表示，利用预训练视觉编码器提取特征。
- 基于潜在扩散模型重建视觉表示，结合跨模态条件与融合模块提升精度。
- 无需外部图像数据，适合需要高精度预测的时序分析场景。
扩散模型近年来成为生成高质量图像的强大框架。尽管已有研究探索其在时间序列预测中的应用，但跨模态建模和有效转化视觉信息以捕捉时序模式仍面临显著挑战。本文提出LDM4TS，一种新颖框架，利用潜在扩散模型强大的图像重建能力实现视觉增强的时间序列预测。不同于引入外部视觉数据，我们首次采用互补变换技术将时间序列转化为多视图视觉表示，使模型能利用预训练视觉编码器丰富的特征提取能力。随后，这些表示通过带有跨模态条件机制和融合模块的潜在扩散模型进行重建。实验结果表明，LDM4TS在多种时间序列预测任务中优于各类专用预测模型。
原文摘要 · Abstract (English)
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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