Conditional Time Series Generation

Conditional time series generation aims to create realistic synthetic time series data conditioned on specific inputs, such as weather patterns or market indicators, for applications like simulation and risk assessment. Recent research heavily utilizes diffusion models and Generative Adversarial Networks (GANs), often incorporating structured noise spaces or leveraging techniques from rough path theory to improve efficiency and accuracy. These advancements are improving the generation of high-quality synthetic data across diverse domains, including finance, energy, and healthcare, leading to better forecasting models and more robust risk management strategies.