TimeGAN for synthetic financial data
Generating synthetic time-series data poses specific challenges above and beyond those encountered when designing GANs for images. In addition to the distribution over variables at any given point, such as pixel values or the prices of numerous stocks, a generative model for time-series data should also learn the temporal dynamics that shape how one sequence of observations follows another. (Refer also to the discussion in Chapter 9, Time-Series Models for Volatility Forecasts and Statistical Arbitrage).
Very recent and promising research by Yoon, Jarrett, and van der Schaar, presented at NeurIPS in December 2019, introduces a novel time-series generative adversarial network (TimeGAN) framework that aims to account for temporal correlations by combining supervised and unsupervised training. The model learns a time-series embedding space while optimizing both supervised and adversarial objectives, which encourage it to adhere to the dynamics...
