  Data augmentation in deep neural networks is the process of generating
artificial data in order to reduce the variance of the classifier with the goal
to reduce the number of errors. This idea has been shown to improve deep neural
network's generalization capabilities in many computer vision tasks such as
image recognition and object localization. Apart from these applications, deep
Convolutional Neural Networks (CNNs) have also recently gained popularity in
the Time Series Classification (TSC) community. However, unlike in image
recognition problems, data augmentation techniques have not yet been
investigated thoroughly for the TSC task. This is surprising as the accuracy of
deep learning models for TSC could potentially be improved, especially for
small datasets that exhibit overfitting, when a data augmentation method is
adopted. In this paper, we fill this gap by investigating the application of a
recently proposed data augmentation technique based on the Dynamic Time Warping
distance, for a deep learning model for TSC. To evaluate the potential of
augmenting the training set, we performed extensive experiments using the UCR
TSC benchmark. Our preliminary experiments reveal that data augmentation can
drastically increase deep CNN's accuracy on some datasets and significantly
improve the deep model's accuracy when the method is used in an ensemble
approach.
