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Irregularly sampled multivariate time series are ubiquitous in variousfields, particularly in healthcare, and exhibit two key characteristics:intra-series irregularity and inter-series discrepancy. Intra-seriesirregularity refers to the fact that time-series signals are often recorded atirregular intervals, while inter-series discrepancy refers to the significantvariability in sampling rates among diverse series. However, recent advances inirregular time series have primarily focused on addressing intra-seriesirregularity, overlooking the issue of inter-series discrepancy. To bridge thisgap, we present Warpformer, a novel approach that fully considers these twocharacteristics. In a nutshell, Warpformer has several crucial designs,including a specific input representation that explicitly characterizes bothintra-series irregularity and inter-series discrepancy, a warping module thatadaptively unifies irregular time series in a given scale, and a customizedattention module for representation learning. Additionally, we stack multiplewarping and attention modules to learn at different scales, producingmulti-scale representations that balance coarse-grained and fine-grainedsignals for downstream tasks. We conduct extensive experiments on widely useddatasets and a new large-scale benchmark built from clinical databases. Theresults demonstrate the superiority of Warpformer over existingstate-of-the-art approaches.

Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series