双上下文模拟检索用于时间序列预测

中文摘要 AI 辅助

大多数长期时间序列预测模型将回看窗口直接映射到完整预测范围，通过单次传递完成。尽管这种方法高效，但它并未明确识别哪些历史状态与不同的未来片段最相关，也未利用这些状态之后的观测结果。模拟预测通过检索与当前状态相似的过去状态并利用其观测到的后续发展来解决这一问题，但单一的最近匹配可能不可靠，且重叠的补丁可能产生冗余候选。我们提出了DuoTS，一种双上下文时间序列预测模型，它利用检索到的证据但不完全依赖于此。DuoTS首先使用并行补丁编码器和线性预测头生成基础预测，然后逐步逐补丁地对其进行细化。每次细化结合两个视图：一个当前上下文，关注最近的令牌并捕捉最新动态；一个细节上下文，提供不同的检索模拟及其后续轨迹。逐补丁细化使模型能够在预测范围内平衡这些视图，并将每个未来片段与适合其距当前时间距离的证据关联起来。在多个真实世界数据集上的实验表明，DuoTS达到了最先进的性能，而消融研究证实了每个上下文的贡献。细化机制也是模型无关的，仅需要编码的回看窗口和未来补丁位置，因此可以集成到现有的预测模型中。

英文摘要

Most long-term time-series forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does not explicitly identify which historical states are most relevant to different future segments or exploit what followed those states. Analog forecasting addresses this by retrieving past states similar to the present and using their observed continuations, but single nearest matches can be unreliable and overlapping patches may produce redundant candidates. We propose DuoTS, a Dual-Context Time Series forecasting model that uses retrieved evidence without relying on it exclusively. DuoTS first produces a base forecast with a parallel patch encoder and linear prediction head, then progressively refines it one future patch at a time. Each refinement combines two views: a current context that attends to recent tokens and captures the latest dynamics, and a detail context that provides distinct retrieved analogs together with their subsequent trajectories. Patch-wise refinement allows the model to balance these views across the forecast horizon and associate each future segment with evidence appropriate to its temporal distance from the present. Experiments on multiple real-world datasets show that DuoTS achieves state-of-the-art performance, while ablations confirm the contribution of each context. The refinement mechanism is also model-agnostic, requiring only an encoded look-back window and the future-patch position, and can therefore be integrated into existing forecasting models.

发表机构

• University of Hildesheim（希尔德斯海姆大学）
• VWFS Data Analytics Research Center (VWFS-DARC)（VWFS数据分析研究中心）

机构由 AI 辅助整理，请以论文原文为准。