Warpformer：一种面向不规则临床时间序列的多尺度建模方法
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
AI总结：
针对不规则临床时间序列的序列内不规则性与序列间差异性问题，提出包含定制输入表示、扭曲模块及多尺度注意力机制的Warpformer方法，在多个基准上取得优于现有SOTA的性能。
摘要：
不规则采样的多变量时间序列在诸多领域中普遍存在，尤其是在医疗健康领域，且呈现出两个关键特征：序列内不规则性与序列间差异性。序列内不规则性指时间序列信号通常以不规则间隔记录，而序列间差异性指不同序列之间的采样率存在显著差异。然而，近期不规则时间序列领域的研究进展主要聚焦于解决序列内不规则性问题，却忽略了序列间差异性的问题。为填补这一空白，我们提出了Warpformer，这是一种充分考虑这两个特征的全新方法。简而言之，Warpformer包含若干关键设计：一种可明确表征序列内不规则性和序列间差异性的特定输入表示、一个能在给定尺度下自适应统一不规则时间序列的扭曲（warping）模块，以及一个用于表示学习的定制化注意力模块。此外，我们堆叠多个扭曲模块与注意力模块以在不同尺度下进行学习，生成能够平衡粗粒度与细粒度信号的多尺度表示，用于下游任务。我们在广泛使用的数据集以及一个基于临床数据库构建的新型大规模基准上开展了大量实验。结果表明，Warpformer的性能优于现有的最先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
