无需先验知识，用社区检测构建动态超图预测多变量时间序列。
Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge
- 通过注意力机制聚类时间序列，生成社区并构造成超图。
- 在多个数据集上实现高阶关系建模，提升预测精度。
- 适合对复杂系统建模、无结构先验的时序分析场景。
超图能捕捉跨领域的实体间高维关系，是理解复杂系统结构与动态的重要工具。然而，在超图结构未知或受限的情况下，如何从时间序列中提取超图表示仍具挑战。本文提出一种无需先验知识的动态超图表示学习方法：首先对时间序列应用社区检测，利用注意力机制获取社区结果，再通过团基技术将其转化为超图；随后，采用动态超图注意力卷积网络（DHACN）对多变量时间序列进行预测。该方法在多个时间序列数据集上验证了其有效性，显著提升了高阶关系建模能力，为无先验条件下的超图表示学习提供了新范式。
原文摘要 · Abstract (English)
Hypergraphs have the capacity to capture higher-dimensional relationships among entities across various domains, making them a subject of growing interest within the research community for understanding the structure and dynamics of complex systems. However, a key challenge is the derivation of hypergraph representations from time series data in situations where the structure of the hypergraph is limited or absent. In this study, we propose a model that constructs a dynamic hypergraph representation for multivariate time series without relying on prior knowledge of the data. This is achieved by applying community detection to the time series and transforming the resulting communities, obtained through an attention mechanism, into a hypergraph using a clique-based technique. Hypergraph representations are derived from different time series datasets, and the resulting hypergraphs are then used by a Dynamic Hypergraph Attention Convolution Network (DHACN) for multivariate time series predictions. This research advances the field of hypergraph representation by introducing a novel approach that is better suited to uncover high-order relationships without prior knowledge.
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