Problem
Research questions and friction points this paper is trying to address.
Irregular Time Series
Domain Generalization
Sampling Pattern Shifts
Multivariate Time Series
Shortcut Learning
Domain Generalization under Sampling Pattern Shifts in Irregular Time Series
🤖 AI Summary
This study addresses the poor domain generalization robustness and model shortcut reliance caused by sampling pattern shifts in irregular time series. To this end, we propose PRISM, a novel framework that first constructs HAR-C, the inaugural controlled sampling shift benchmark. Methodologically, PRISM employs unsupervised dual-view representation learning to disentangle intrinsic features from sampling representations. This is further combined with an adversarial and diversity-enhanced supervised training strategy to eliminate shortcut dependencies on specific sampling patterns. Experimental results demonstrate that the proposed approach significantly improves predictive robustness against unseen sampling shifts across both controlled and real-world benchmarks, outperforming existing domain generalization methods.
📝 Abstract
Irregularly sampled multivariate time series (ISMTS) are prevalent in real-world applications, where both observation times and available measurements can vary substantially across domains. While recent models increasingly exploit such sampling information for prediction, its robustness under sampling pattern shifts remains underexplored. We introduce HAR-C, to the best of our knowledge the first controlled benchmark for sampling pattern shifts in ISMTS, and show that sampling shifts alone can substantially degrade performance, induce sampling-specific shortcuts, and remain challenging for existing domain generalization (DG) methods. Motivated by these findings, we propose PRISM, a DG framework that first learns complementary feature-centric and sampling-centric representations without task labels, and subsequently performs robust supervised training across diverse sampling variations to discourage brittle shortcut reliance. Extensive experiments on controlled and real-world ISMTS benchmarks demonstrate that PRISM consistently improves robustness to unseen sampling shifts over existing methods. Our code is available at https://anonymous.4open.science/r/PRISM.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Domain Generalization
Irregular Time Series
Sampling Pattern Shifts
Representation Learning
PRISM
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