Dynamic Channel-entity Bipartite Graph for Irregular Multi-channel Time Series

Irregularly sampled multi-channel time series exhibit asynchronous observations, sparsity, and uneven sampling intervals, while their sampling patterns may themselves convey task-relevant information. Existing graph-based methods organize interactions by learning channel dependencies over fully connected candidate graphs, constructing channel relations from semantic priors, or building channel-time graphs within predefined temporal patches. However, fixed patching may leave some patches with insufficient information and split key temporal dynamics across patch boundaries, while dense or prior-based graphs may propagate irrelevant information when relations are insufficiently filtered or priors are mismatched. To address these challenges, we propose the Reference-Entity-Augmented Dynamic Graph model (REA-DG), which organizes observation-driven local interactions through a sample entity. REA-DG constructs an entity-channel bipartite graph in which the sample entity connects only to currently observed channels and mediates their interactions. To complement sparse local information, shared learnable reference entities connect to all channels through channel-specific learnable edges, injecting context learned from the training data into message passing. Experiments demonstrate that REA-DG achieves state-of-the-art performance across financial forecasting, clinical time-series classification, and network-traffic forecasting.