VFEM: 视觉特征赋能的多变量时间序列预测与跨模态融合
VFEM: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion
AI总结：
提出VFEM模型，利用预训练大视觉模型通过跨模态注意力融合视觉与时间特征，仅训练7.45%参数即可捕捉跨变量依赖，提升多变量时间序列预测性能。
摘要：
大型时间序列基础模型通常采用通道独立架构来处理不同的数据维度，但这种设计忽略了关键的跨通道依赖关系。同时，现有的跨模态方法主要依赖文本模态，使得视觉模型的空间模式识别能力在时间序列分析中未被充分探索。为了解决这些局限性，我们提出了VFEM，一种利用预训练大视觉模型（LVM）捕获复杂跨变量模式的跨模态预测模型。VFEM将多变量时间序列转换为视觉表示，使LVM能够感知通道独立模型未显式建模的空间关系。通过双分支架构，视觉和时间特征被独立提取，然后通过跨模态注意力融合，使两种模态的互补信息增强预测。通过冻结LVM并仅训练总参数的7.45%，VFEM在多个基准上取得了竞争性能，为多变量时间序列预测提供了新视角。
英文摘要：
Large time series foundation models often adopt channel-independent architectures to handle varying data dimensions, but this design ignores crucial cross-channel dependencies. Meanwhile, existing cross-modal methods predominantly rely on textual modalities, leaving the spatial pattern recognition capabilities of vision models underexplored for time series analysis. To address these limitations, we propose VFEM, a cross-modal forecasting model that leverages pre-trained large vision models (LVMs) to capture complex cross-variable patterns. VFEM transforms multivariate time series into visual representations, enabling LVMs to perceive spatial relationships that are not explicitly modeled by channel-independent models. Through a dual-branch architecture, visual and temporal features are independently extracted and then fused via cross-modal attention, allowing complementary information from both modalities to enhance forecasting. By freezing the LVM and training only 7.45% of the total parameters, VFEM achieves competitive performance on multiple benchmarks, offering a new perspective on multivariate time series forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
