发表机构
East China Normal University（华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MUSE提出依赖感知适配框架，基于冻结的MAE视觉骨干，通过VCR和TPR模块建模跨变量与跨周期依赖，在10个数据集上取得最先进预测性能。
AI 中文摘要
多元时间序列预测对许多现实世界应用至关重要。近期的大型视觉模型（LVM）通过将跨领域视觉先验迁移到时间序列预测，提供了一种有前景的范式。然而，现有的基于LVM的方法面临两个关键挑战：平衡独立视觉表示空间与跨变量依赖建模，以及将预训练于自然图像的视觉骨干适配到时间序列图像的独特时间语义。为应对这些挑战，我们提出了MUSE，一个构建于完全冻结的预训练MAE之上的依赖感知适配框架。首先，变量上下文精炼模块（VCR）在保留独立视觉空间的同时，聚合每个变量内的共享时间信息并建模跨变量上下文依赖。其次，时间-周期精炼模块（TPR）在不同编码器深度执行轻量级精炼，并显式建模跨周期时间依赖和周期内周期依赖。这两个模块独立产生预测，并通过一个可学习的预测级门控进行融合。在10个真实世界数据集上的实验表明，MUSE实现了最先进的性能。
英文摘要
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.
