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Improving Time Series Generation of GANs through Soft Dynamic Time Warping Loss

With the rising popularity of Generative Adversarial Networks (GANs) in generating synthetic data, time series are no exception to this trend. In this work, we propose two novel loss functions sDTW-p and sDTW-m based on SoftDynamic Time Warping that can be used to improve the generated time series without modifications to the existing architecture. We also present the first evaluation of the generated samples across different sequence length. Lastly, we show empirically that the result of leveraging our loss function can lead to a 9% improvement according to our metric.