Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time
  Series
Jiawen Zhang Shun Zheng Wei Cao Jiang Bian Jia Li
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.