HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting

TL;DR

HyperIMTS introduces a hypergraph neural network that effectively models irregular multivariate time series by capturing dependencies through a unified hypergraph structure, leading to accurate forecasting with low computational cost.

Contribution

It proposes a novel hypergraph-based approach for IMTS forecasting that unifies temporal and variable dependencies without padding or bipartite graph limitations.

Findings

Achieves competitive forecasting accuracy.

Maintains low computational cost.

Effectively models unaligned observations.

Abstract

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.