Heterogeneous Pattern Representation and Interaction for General Irregular Multivariate Time Series Analysis

Time series pattern machines (TSPMs) have been extensively studied for their ability to capture universal patterns and support diverse downstream tasks. However, existing TSPMs primarily target regularly sampled time series. In real-world scenarios, missing observations and differing sampling schedules often give rise to irregular multivariate time series (IMTS), characterized by uneven sampling intervals and asynchronous observations across variates. Developing TSPMs for IMTS is therefore more challenging because it requires capturing heterogeneous patterns, including temporal variations arising from intra-series irregularity and cross-variate dependencies arising from inter-series asynchrony. This calls for complementary representations of these patterns and effective interactions among them. To address these challenges, we propose **HeteroNet**, a TSPM designed for IMTS through **hetero**geneous pattern representation and interaction. It combines (1) Observation Encoding, which preserves the temporal context of individual observations, with (2) Sparse Pattern Tokenization, which explicitly represents recent states, changes over elapsed intervals, and variate histories. Then, (3) Pattern Interaction models dependencies among these heterogeneous patterns within and across asynchronous variates. Finally, (4) Kernel Fusion integrates the resulting representations with temporal kernel summaries of all observations. Extensive experiments across five IMTS analysis tasks show that HeteroNet achieves state-of-the-art performance, outperforming both general-purpose and task-specific baselines.