HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting
This addresses forecasting challenges in domains like healthcare or finance where data is irregular, but it is incremental as it builds on existing graph-based methods.
The paper tackles the problem of forecasting irregular multivariate time series with irregular time intervals and unaligned observations by proposing HyperIMTS, a hypergraph neural network that captures temporal and variable dependencies in a unified form, achieving competitive performance with low computational cost.
Irregular multivariate time series (IMTS) are characterized by irregular time intervals within variables and unaligned observations across variables, posing challenges in learning temporal and variable dependencies. Many existing IMTS models either require padded samples to learn separately from temporal and variable dimensions, or represent original samples via bipartite graphs or sets. However, the former approaches often need to handle extra padding values affecting efficiency and disrupting original sampling patterns, while the latter ones have limitations in capturing dependencies among unaligned observations. To represent and learn both dependencies from original observations in a unified form, we propose HyperIMTS, a Hypergraph neural network for Irregular Multivariate Time Series forecasting. Observed values are converted as nodes in the hypergraph, interconnected by temporal and variable hyperedges to enable message passing among all observations. Through irregularity-aware message passing, HyperIMTS captures variable dependencies in a time-adaptive way to achieve accurate forecasting. Experiments demonstrate HyperIMTS's competitive performance among state-of-the-art models in IMTS forecasting with low computational cost.