STRGCN：面向不规则多元时间序列预测的异步时空依赖捕获方法
STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting
AI总结：
STRGCN通过将不规则多元时间序列建模为全连接图并结合分层“三明治”结构，在不进行预对齐的情况下直接捕获异步时空依赖，在四个公开数据集上取得最先进精度与高效资源表现。
摘要：
不规则多元时间序列（IMTS）在现实世界的许多领域应用中普遍存在，其中不同的传感器频率和异步测量带来了显著的建模挑战。现有解决方案通常依赖预对齐策略来规范化数据，这可能扭曲内在模式并增加计算和内存开销。针对这些局限性，我们提出了STRGCN，一种时空关系图卷积网络，它避免预对齐，通过将IMTS表示为全连接图来直接捕获其中的复杂相互依赖关系。每个观测值被表示为一个节点，使模型能够通过映射所有节点间关系来有效处理错位的时间戳，从而忠实保留数据的异步特性。此外，我们通过一种分层的“三明治”结构增强了该模型，该结构策略性地聚合节点以优化图嵌入，在保持详细的局部和全局上下文的同时降低计算开销。在四个公开数据集上的大量实验表明，STRGCN实现了最先进的准确性、具有竞争力的内存占用和训练速度。
英文摘要：
Irregular multivariate time series (IMTS) are prevalent in real-world applications across many fields, where varying sensor frequencies and asynchronous measurements pose significant modeling challenges. Existing solutions often rely on a pre-alignment strategy to normalize data, which can distort intrinsic patterns and escalate computational and memory demands. Addressing these limitations, we introduce STRGCN, a Spatio-Temporal Relational Graph Convolutional Network that avoids pre-alignment and directly captures the complex interdependencies in IMTS by representing them as a fully connected graph. Each observation is represented as a node, allowing the model to effectively handle misaligned timestamps by mapping all inter-node relationships, thus faithfully preserving the asynchronous nature of the data. Moreover, we enhance this model with a hierarchical ``Sandwich'' structure that strategically aggregates nodes to optimize graph embeddings, reducing computational overhead while maintaining detailed local and global context. Extensive experiments on four public datasets demonstrate that STRGCN achieves state-of-the-art accuracy, competitive memory usage and training speed.
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。
