Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge

ArXi:2605.22540v1 Announce Type: cross 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.