发表机构
East China Normal University（华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对协变量感知时间序列预测，提出XMatch模型，通过树结构匹配外生与内生模式，自适应选择匹配条件，利用历史对应增强预测，在12个数据集上超越现有方法。
AI 中文摘要
未来的外生变量为预测内生时间序列提供了有价值的信息。现有的协变量感知方法主要学习外生变量对内生变量的直接影响。然而，这些影响可能很复杂，并随外生变量的模式而变化，难以捕捉。超越这一视角，我们观察到给定的外生模式通常仅与一小部分内生响应模式共同出现。这些关联激发了一种策略，即匹配未来和历史上的外生模式，并利用相应的内生模式来增强预测。然而，在具有多个外生变量的现实世界预测场景中，每个外生变量为匹配提供了不同的维度，这给该策略在精确匹配和充分的历史支持之间造成了困境。为弥合这一差距，我们提出了XMatch（外生匹配），一种协变量感知的预测模型，通过树结构匹配过程实现上述策略，该过程自适应地调整用作匹配条件的外生变量的数量。具体来说，我们首先引入ProtoTree Creator，它将外生与内生模式之间的历史对应关系组织成ProtoTree，其更深层次包含额外的外生变量用于匹配。对于预测，我们设计了ProtoTree Matcher，它使用未来的外生变量查询ProtoTree，并基于外生模式相似性和历史支持自适应地确定使用多少外生变量进行匹配。最后，匹配的内生模式被用作明确的历史证据以增强预测。在12个真实世界数据集上的大量实验表明，XMatch优于最先进的基线方法。
英文摘要
Future exogenous variables provide valuable information for forecasting endogenous time series. Existing covariate-aware methods primarily learn the direct influence of exogenous variables on endogenous variables. However, these effects can be complex and change with the pattern of the exogenous variables, making them difficult to capture. Beyond this perspective, we observe that a given exogenous pattern often co-occurs with only a small set of endogenous response patterns. These associations motivate a strategy that matches future and historical exogenous patterns and uses the corresponding endogenous patterns to enhance forecasting. However, in real-world forecasting scenarios with multiple exogenous variables, each exogenous variable provides a distinct dimension for matching, creating a dilemma for this strategy between precise matching and sufficient historical support. To bridge this gap, we propose XMatch (EXogenous MATCHing), a covariate-aware forecasting model that realizes the aforementioned strategy through a tree-structured matching process that adaptively adjusts the number of exogenous variables used as matching conditions. Specifically, we first introduce the ProtoTree Creator, which organizes historical correspondences between exogenous and endogenous patterns into a ProtoTree, whose deeper levels incorporate additional exogenous variables for matching. For forecasting, we then design the ProtoTree Matcher, which uses future exogenous variables to query the ProtoTree and adaptively determines how many exogenous variables to use for matching based on exogenous pattern similarity and historical support. Finally, the matched endogenous patterns are used as explicit historical evidence to enhance forecasting. Extensive experiments on 12 real-world datasets demonstrate that XMatch outperforms state-of-the-art baselines.
