Time Series Data Augmentation using Time-Warped Auto-Encoders

The recent eminent success of deep learning models has triggered a lot of interest in data augmentation studies. The main challenge of data-hungry models is that they are bound to overfitting and require massive amounts of data to be trained on to achieve optimal levels of convergence. It is of high importance for any data oversampling algorithm to produce synthetic data samples that are not only ...