  Irregularly sampled time series are increasingly prevalent, particularly in
medical domains. While various specialized methods have been developed to
handle these irregularities, effectively modeling their complex dynamics and
pronounced sparsity remains a challenge. This paper introduces a novel
perspective by converting irregularly sampled time series into line graph
images, then utilizing powerful pre-trained vision transformers for time series
classification in the same way as image classification. This method not only
largely simplifies specialized algorithm designs but also presents the
potential to serve as a universal framework for time series modeling.
Remarkably, despite its simplicity, our approach outperforms state-of-the-art
specialized algorithms on several popular healthcare and human activity
datasets. Especially in the rigorous leave-sensors-out setting where a portion
of variables is omitted during testing, our method exhibits strong robustness
against varying degrees of missing observations, achieving an impressive
improvement of 42.8% in absolute F1 score points over leading specialized
baselines even with half the variables masked. Code and data are available at
https://github.com/Leezekun/ViTST
