Irregularly Sampled Multivariate Time Series Classification: A Graph Learning Approach

To date, graph-based learning methods are proven to be effective for modeling spatial and structural dependencies. However, when applied to IS-MTS, they encounter three major challenges due to the complex data characteristics of IS-MTS: 1) variable time intervals between observations; 2) asynchronous time points across dimensions; and 3) a lack of prior knowledge of connectivity structure for message propagation. To fill these gaps, we propose a multivariate temporal graph network to coherently capture structural interactions, learn temporal dependencies, and handle challenging characteristics of IS-MTS data. Specifically, we first build a multivariate interaction module to handle frequent missing values and extract the graph structure relation automatically. Second, we design a novel adjacent graph propagation mechanism to aggregate the neighbor information from multistep snapshots. Third, we construct a masked temporal-aware attention module to explicitly consider the timestamp context and interval irregularity. Based on an extensive experimental evaluation, we demonstrate the superior performance of the proposed method.