Vision-Enhanced Time Series Forecasting via Latent Diffusion Models

2025-02-16

AI Summary

To address the challenges of modeling long-range temporal dependencies and fusing multi-source information in time series, this paper proposes a novel time-series-to-vision self-transformation paradigm: raw sequences are mapped into multi-view image representations (e.g., Gramian Angular Field, Markov Transition Field), and discriminative visual features are extracted using a pre-trained Vision Transformer (ViT). Subsequently, a cross-modal conditional Latent Diffusion Model (LDM) is designed to jointly model visual priors and sequential dynamics—without requiring external image inputs. Key innovations include: (i) the first purely time-series-driven visual representation generation mechanism; (ii) a cross-modal conditional LDM architecture; and (iii) a feature-level fusion module. Extensive experiments on multiple benchmark datasets demonstrate that our method consistently outperforms state-of-the-art models—including Informer, Autoformer, and PatchTST—with an average 12.7% reduction in MAE. Notably, it exhibits superior generalization and robustness in long-horizon forecasting (h ≥ 96).

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Enhances time series forecasting with visual data
• Transforms time series into multi-view visual representations
• Utilizes latent diffusion models for cross-modal modeling

Innovation

• Leverages latent diffusion models
• Converts time series visually
• Uses cross-modal conditioning mechanism