Generative Adversarial Nets for Synthetic Time Series Data This repo shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests. We replicate the 2019 NeurIPS Time-Series GAN paper by Jinsung Yoon, et al., to illustrate the approach and demonstrate the results. The material is based on the 2 nd edition of my book on Machine Learning for Trading) (see GitHub repo). Content 1. Generative adversarial networks for synthetic data Comparing generative and discriminative models Adv

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