Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge
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, obt...
| Authors | Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco et al. |
|---|---|
| Primary category | cs.CE |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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