TimeGAN implements a framework for generating synthetic time-series data using adversarial training between a generator and discriminator network. The repository includes implementations for multiple dataset types (synthetic sine, stock, and energy data), evaluation metrics including PCA/t-SNE visualization, discriminative scoring via post-hoc classifiers, and predictive metrics. The model architecture supports multiple deep learning modules including GRU, LSTM, and LSTM with layer normalization.