  In recent years, neural networks achieved much success in various
applications. The main challenge in training deep neural networks is the lack
of sufficient data to improve the model's generalization and avoid overfitting.
One of the solutions is to generate new training samples. This paper proposes a
novel data augmentation method for time series based on Dynamic Time Warping.
This method is inspired by the concept that warped parts of two time series
have similar temporal properties and therefore, exchanging them between the two
series generates a new training sample. The proposed method selects an element
of the optimal warping path randomly and then exchanges the segments that are
aligned together. Exploiting the proposed approach with recently introduced
ResNet reveals improved results on the 2018 UCR Time Series Classification
Archive. By employing Gradient-weighted Class Activation Mapping (Grad-CAM) and
Multidimensional Scaling (MDS), we manifest that our method extract more
discriminant features out of time series.
