Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks

This paper utilizes a Conditional Wasserstein GAN (CWGAN) and augment it with an error penalty term, leading to a new generative model which aims to generate high-quality synthetic time series data, called GenF, which outperforms a diverse range of state-of-the-art benchmarks and classical approaches.