Multivariate time series forecasting (MTSF) plays a vital role in numerous real-world applications, yet existing models remain constrained by their reliance on a limited historical context. This limitation prevents them from effectively capturing global periodic patterns that often span cycles significantly longer than the input horizon - despite such patterns carrying strong predictive signals. Naive solutions, such as extending the historical window, lead to severe drawbacks, including overfitting, prohibitive computational costs, and redundant information processing. To address these challenges, we introduce the Global Temporal Retriever (GTR), a lightweight and plug-and-play module designed to extend any forecasting model's temporal awareness beyond the immediate historical context. GTR maintains an adaptive global temporal embedding of the entire cycle and dynamically retrieves and aligns relevant global segments with the input sequence. By jointly modeling local and global dependencies through a 2D convolution and residual fusion, GTR effectively bridges short-term observations with long-term periodicity without altering the host model architecture. Extensive experiments on six real-world datasets demonstrate that GTR consistently delivers state-of-the-art performance across both short-term and long-term forecasting scenarios, while incurring minimal parameter and computational overhead. These results highlight GTR as an efficient and general solution for enhancing global periodicity modeling in MTSF tasks. Code is available at this repository: https://github.com/macovaseas/GTR.

翻译：多元时间序列预测在众多实际应用中发挥着关键作用，但现有模型仍受限于其对有限历史上下文的依赖。这一局限使得模型难以有效捕捉通常跨越远长于输入时域的周期性全局模式——尽管此类模式携带强烈的预测信号。简单的解决方案，如扩展历史窗口，会带来严重缺陷，包括过拟合、高昂的计算成本以及冗余信息处理。为应对这些挑战，我们提出了全局时序检索器，一种轻量级即插即用模块，旨在将任何预测模型的时序感知能力扩展到即时历史上下文之外。GTR 维护整个周期的自适应全局时序嵌入，并动态检索相关全局片段与输入序列对齐。通过二维卷积和残差融合联合建模局部与全局依赖关系，GTR 在不改变宿主模型架构的情况下，有效桥接了短期观测与长期周期性。在六个真实数据集上的大量实验表明，GTR 在短期和长期预测场景中均能持续提供最先进的性能，同时仅引入极少的参数和计算开销。这些结果凸显了 GTR 作为增强多元时间序列预测任务中全局周期性建模的高效通用解决方案。代码已发布于该仓库：https://github.com/macovaseas/GTR。