MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting

2026-09-21

AI Summary

为解决多变量时间序列预测中跨变量依赖建模和视觉骨干适应问题，提出MUSE框架，通过两个模块分别处理变量上下文和周期性时序依赖，实现高效预测。

Abstract

Multivariate time-series forecasting is essential to many real-world applications. Recent large vision models (LVMs) offer a promising paradigm by transferring cross-domain visual priors to time-series forecasting. However, existing LVM-based methods face two key challenges: balancing independent visual representation spaces with cross-variable dependency modeling, and adapting vision backbones pretrained on natural images to the distinct temporal semantics of time-series images. To address these challenges, we propose MUSE, a dependency-aware adaptation framework built on a fully frozen pretrained MAE. First, the Variable Context Refinement Module (VCR) aggregates shared temporal information within each variable and models cross-variable contextual dependencies while preserving independent visual spaces. Second, the Temporal-Periodic Refinement Module (TPR) performs lightweight refinement at different encoder depths and explicitly models across-period temporal dependencies and within-period periodic dependencies. The two modules independently produce forecasts, which are fused through a learnable prediction-level gate. Experiments on 10 real-world datasets demonstrate that MUSE achieves state-of-the-art performance.

Problem

• Multivariate Time Series Forecasting
• Dependency Modeling
• Vision Backbone Adaptation

Innovation

• Dependency-Aware Adaptation
• Multivariate Time Series Forecasting
• Frozen Vision Backbone
• Cross-Variable Dependencies
• Temporal-Periodic Dependencies