Success of training deep learning models largely depends on the amount and quality of training data. Although numerous data augmentation techniques have already been pro- posed for certain domains such as computer vision where simple schemes such as rotation and flipping have been shown to be effective, other domains such as time-series data have a relatively smaller set of augmentation techniques readily available. Besides, data imbalance is a phenomenon that is often observed in real-world data. However, a simple oversampling may make a model vulnerable to overfitting, so a proper data augmentation is desired. To tackle these problems, we propose a data augmentation method that utilizes latent vectors of an autoencoder in a novel way. When input data is perturbed in its latent space, the reconstructed input data retains similar properties to the original one. On the other hand, adversarial augmentation is a technique to train robust deep neural networks against un- foreseen data shifts or corruptions by providing a downstream model with difficult samples to predict. Our method adversarily perturbs input data in its latent space so that the aug- mented data is diverse and conducive to reducing test error of a downstream model. The experimental results demonstrate that our method achieves a right balance in significantly modifying the input data to help generalization while keeping the realism of it.