Irregularly sampled time series are becoming increasingly prevalent in
various domains, especially in medical applications. Although different
highly-customized methods have been proposed to tackle irregularity, how to
effectively model their complicated dynamics and high sparsity is still an open
problem. This paper studies the problem from a whole new perspective:
transforming irregularly sampled time series into line graph images and
adapting powerful vision transformers to perform time series classification in
the same way as image classification. Our approach largely simplifies algorithm
designs without assuming prior knowledge and can be potentially extended as a
general-purpose framework. Despite its simplicity, we show that it
substantially outperforms state-of-the-art specialized algorithms on several
popular healthcare and human activity datasets. Especially in the challenging
leave-sensors-out setting where a subset of variables is masked during testing,
the performance improvement is up to 54.0\% in absolute F1 score points. Our
code and data are available at https://github.com/Leezekun/ViTST.

本文旨在利用图像分类的方式，将不规则采样的时间序列转换为线图像，应用于医疗应用中的时间序列分类问题，大大简化了算法设计，同时在多个数据集上显示出良好的性能，特别是在具有挑战性的离散传感器设置中，绝对 F1 分数提高了 54.0％。