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.

翻译：超图能够捕捉跨不同领域实体间的高维关系，使其成为研究社区中理解复杂系统结构与动力学日益关注的焦点。然而，一个关键挑战在于当超图结构受限或缺失时，如何从时间序列数据推导超图表示。本研究提出一种模型，可在不依赖数据先验知识的情况下，为多变量时间序列构建动态超图表示。该模型通过对时间序列应用社区检测，并将基于注意力机制获得的社区结果通过团簇技术转化为超图。通过不同时间序列数据集推导超图表示，并利用动态超图注意力卷积网络（DHACN）进行多变量时间序列预测。本研究通过引入一种无需先验知识即可更优揭示高阶关系的新方法，推动了超图表示领域的发展。