  Irregularly sampled multivariate time series are ubiquitous in various
fields, particularly in healthcare, and exhibit two key characteristics:
intra-series irregularity and inter-series discrepancy. Intra-series
irregularity refers to the fact that time-series signals are often recorded at
irregular intervals, while inter-series discrepancy refers to the significant
variability in sampling rates among diverse series. However, recent advances in
irregular time series have primarily focused on addressing intra-series
irregularity, overlooking the issue of inter-series discrepancy. To bridge this
gap, we present Warpformer, a novel approach that fully considers these two
characteristics. In a nutshell, Warpformer has several crucial designs,
including a specific input representation that explicitly characterizes both
intra-series irregularity and inter-series discrepancy, a warping module that
adaptively unifies irregular time series in a given scale, and a customized
attention module for representation learning. Additionally, we stack multiple
warping and attention modules to learn at different scales, producing
multi-scale representations that balance coarse-grained and fine-grained
signals for downstream tasks. We conduct extensive experiments on widely used
datasets and a new large-scale benchmark built from clinical databases. The
results demonstrate the superiority of Warpformer over existing
state-of-the-art approaches.
