Title: Time Series are Images: Vision Transformer for Irregularly Sampled Time Series. Abstract: Irregularly sampled time series are often observed in medical applications. Highly customized models have been developed to tackle the irregularity. In this work, we propose a simple yet effective approach that transforms irregularly sampled time series into line graph images and adapts vision transformers to perform time series classification in a way similar to image classification. Our approach simplifies the model design without assuming prior knowledge. Despite its simplicity, we show that it is able to outperform 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 are masked during testing. We hope this work could provide beneficial insight into leveraging fast-evolving computer vision techniques in the time series analysis domain.