vLinear：一种强大的多变量时间序列预测线性模型
vLinear: A Powerful Linear Model for Multivariate Time Series Forecasting
AI总结：
vLinear通过vecTrans模块和WFMLoss目标，实现高效的多变量时间序列预测，提升预测精度并加速推理过程。
摘要：
在本文中，我们提出了vLinear，一种高效且有效的基于线性模型的多变量时间序列预测器，其包含两个组件：vecTrans模块和WFMLoss目标。许多最先进的预测器依赖于自注意力或其变种来捕捉多变量相关性，通常导致计算复杂度为O(N²)，其中N是变量数量。为了解决这个问题，我们提出了vecTrans，一个轻量级模块，利用可学习的向量来建模多变量相关性，将复杂度降低到O(N)。值得注意的是，vecTrans可以无缝集成到基于Transformer的预测器中，提供高达5倍的推理加速和一致的性能提升。此外，我们引入了WFMLoss（加权流匹配损失）作为目标。与典型的速度导向流匹配目标不同，我们证明了以最终系列为导向的公式在预测准确性上显著优于传统方法。WFMLoss还结合了路径和时间范围加权策略，以关注更可靠的路径和时间范围。经验上，vLinear在22个基准和124种预测设置中均取得了最先进的性能。此外，WFMLoss作为有效的即插即用目标，一致地提升了现有预测器的性能。代码可在https://anonymous.4open.science/r/vLinear获取。
英文摘要：
In this paper, we present \textbf{vLinear}, an effective yet efficient \textbf{linear}-based multivariate time series forecaster featuring two components: the \textbf{v}ecTrans module and the WFMLoss objective. Many state-of-the-art forecasters rely on self-attention or its variants to capture multivariate correlations, typically incurring $\mathcal{O}(N^2)$ computational complexity with respect to the number of variates $N$. To address this, we propose vecTrans, a lightweight module that utilizes a learnable vector to model multivariate correlations, reducing the complexity to $\mathcal{O}(N)$. Notably, vecTrans can be seamlessly integrated into Transformer-based forecasters, delivering up to 5$\times$ inference speedups and consistent performance gains. Furthermore, we introduce WFMLoss (Weighted Flow Matching Loss) as the objective. In contrast to typical \textbf{velocity-oriented} flow matching objectives, we demonstrate that a \textbf{final-series-oriented} formulation yields significantly superior forecasting accuracy. WFMLoss also incorporates path- and horizon-weighted strategies to focus learning on more reliable paths and horizons. Empirically, vLinear achieves state-of-the-art performance across 22 benchmarks and 124 forecasting settings. Moreover, WFMLoss serves as an effective plug-and-play objective, consistently improving existing forecasters. The code is available at https://anonymous.4open.science/r/vLinear.
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。
