Time Series Simulation by Conditional Generative Adversarial Net

Understand

Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation.

• In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data.
• The conditions can be both categorical and continuous variables containing different kinds of auxiliary information.
• Our simulation studies show that CGAN is able to learn different kinds of normal and heavy tail distributions, as well as dependent structures of different time series and it can further generate conditional predictive distributions consistent with the training data distributions.