  Labeling time series data is an expensive task because of domain expertise
and dynamic nature of the data. Hence, we often have to deal with limited
labeled data settings. Data augmentation techniques have been successfully
deployed in domains like computer vision to exploit the use of existing labeled
data. We adapt one of the most commonly used technique called MixUp, in the
time series domain. Our proposed, MixUp++ and LatentMixUp++, use simple
modifications to perform interpolation in raw time series and classification
model's latent space, respectively. We also extend these methods with
semi-supervised learning to exploit unlabeled data. We observe significant
improvements of 1\% - 15\% on time series classification on two public
datasets, for both low labeled data as well as high labeled data regimes, with
LatentMixUp++.
