The article discusses the use of synthetic time series data and introduces DoppelGANger, a generative adversarial network (GAN) model for generating such data. It highlights the challenges in creating synthetic time series data due to the additional dimension of time and trends across time. The PyTorch implementation of DoppelGANger is presented as an open-source solution that provides flexibility and high-quality synthetic data generation. The sample usage demonstrates how to train and generate synthetic data using both pandas DataFrame and numpy arrays inputs. Results show that the PyTorch implementation produces high-fidelity synthetic data with temporal correlations at different scales, comparable to the original TensorFlow 1 implementation but with a significant runtime speedup (~40x).