arXiv 2509.233132026-03-12cs.LG

ASTGI：自适应时空图交互用于不规则多变量时间序列预测

ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting

School of Data Science and Engineering, East China Normal University, Shanghai, China（东华大学数据科学与工程学院，上海，中国）；Engineering Research Center of Blockchain Data Management, Ministry of Education, China（教育部区块链数据管理工程研究中心，中国）

Xvyuan Liu, Xiangfei Qiu, Hanyin Cheng, Xingjian Wu, Chenjuan Guo, Bin Yang, Jilin Hu

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AI总结：

本文提出ASTGI框架，通过自适应时空图交互解决不规则多变量时间序列预测中的数据表示和动态依赖捕捉问题，实验表明其优于现有方法。

摘要：

不规则多变量时间序列（IMTS）在医疗和金融等关键领域普遍存在，准确预测对前瞻性决策至关重要。然而，IMTS的异步采样和不规则间隔给现有方法带来两个核心挑战：（1）如何准确表示不规则时间序列的原始信息而不引入数据失真；（2）如何有效捕捉观测点之间的复杂动态依赖关系。为解决这些问题，我们提出自适应时空图交互（ASTGI）框架。具体而言，该框架首先使用时空点表示模块将每个离散观测编码为可学习的时空嵌入空间中的点。其次，邻域自适应图构建模块通过最近邻搜索自适应地为每个点构建因果图。随后，时空动态传播模块通过生成消息和计算交互权重，迭代更新这些自适应因果图上的信息。最后，基于查询点的预测模块通过聚合邻近信息为新查询点生成最终预测。在多个基准数据集上的广泛实验表明，ASTGI优于各种现有方法。

英文摘要：

Irregular multivariate time series (IMTS) are prevalent in critical domains like healthcare and finance, where accurate forecasting is vital for proactive decision-making. However, the asynchronous sampling and irregular intervals inherent to IMTS pose two core challenges for existing methods: (1) how to accurately represent the raw information of irregular time series without introducing data distortion, and (2) how to effectively capture the complex dynamic dependencies between observation points. To address these challenges, we propose the Adaptive Spatio-Temporal Graph Interaction (ASTGI) framework. Specifically, the framework first employs a Spatio-Temporal Point Representation module to encode each discrete observation as a point within a learnable spatio-temporal embedding space. Second, a Neighborhood-Adaptive Graph Construction module adaptively builds a causal graph for each point in the embedding space via nearest neighbor search. Subsequently, a Spatio-Temporal Dynamic Propagation module iteratively updates information on these adaptive causal graphs by generating messages and computing interaction weights based on the relative spatio-temporal positions between points. Finally, a Query Point-based Prediction module generates the final forecast by aggregating neighborhood information for a new query point and performing forecasting. Extensive experiments on multiple benchmark datasets demonstrate that ASTGI outperforms various state-of-the-art methods.

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。