  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 the same temporal properties. Exploiting the proposed approach with
recently-introduced ResNet reveals the improvement of results on the 2018 UCR
Time Series Classification Archive.
