Labels are typically sparse in real-world time series due to the high annotation cost. Recently, consistency regularization techniques have been used to generate artificial labels from unlabeled augmented instances. To fully exploit the sequential characteristic of time series in consistency regularization, we propose a novel method of data augmentation called *context-attached augmentation*, which adds preceding and succeeding instances to a target instance to form its augmented instance. Unlik…