Beyond Sequences: A Dynamic Hierarchical Heterogeneous Spatio-Temporal Graph for Irregular Multivariate Time Series Forecasting

Abstract

Irregular Multivariate Time Series (IMTS) analysis is a challenging task as asynchronous irregular sampling disrupts intra-variable temporal consistency and cross-variable alignment. Most existing methods model multivariate correlations at either the variable or observation level in static ways. They suffer from correlation loss or cross-variable misalignment inevitably. In this paper, we propose a novel method DyH2-STGraph, Dynamic Hierarchical Heterogeneous Spatio-Temporal Graph, for IMTS forecasting. Within the graph, irregular observations are represented as nodes with spatio-temporal coordinates and observed values, connected with other neighbor observation nodes dynamically by spatio-temporal neighbor selection, while variables are treated as hyper nodes, connected with their constituent observation nodes as well as other variable nodes. The multivariate correlations are classified into the following three categories in specific manners: (1) Intra-variable coarse-to-fine correlations between variable nodes and their constituent observation nodes captured by hierarchical message propagation, (2) Inter-variable fine-grained correlations among neighboring observation nodes captured by spatio-temporal attention, and (3) Inter-variable coarse-grained correlations among variable nodes captured by attention. Extensive experiments on four benchmark datasets demonstrate that DyH2-STGraph significantly outperforms state-of-the-art methods while maintaining competitive efficiency.