MUSE：冻结MAE视觉骨干的多变量时间序列预测适配框架
MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting
用完全冻结的MAE视觉模型做时序预测，加上VCR和TPR两个模块，在10个数据集上跑赢了现有方法。
MUSE 提出在完全冻结的预训练 MAE 视觉骨干上进行多变量时间序列预测，将自然图像预训练的视觉先验迁移到时序任务。框架包含 VCR（Variable Context Refinement Module）和 TPR（Temporal-Periodic Refinement Module）两个模块：VCR 在保留各变量独立视觉空间的同时建模跨变量上下文依赖，TPR 在不同编码器深度显式建模跨周期与周期内的时间依赖。两个模块各自独立产出预测，再经可学习的预测级门控融合。在 10 个真实数据集的实验中，MUSE 取得了 state-of-the-art 性能。
MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting
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.
