Intern Spotlight: HRT AI Labs Project

This post details the exploration and application of diffusion models for generating stock return sequences. It covers techniques for conditioning generation on stock metadata (e.g., volume, volatility), modeling joint cross-sectional returns by treating stocks as channels, and using inpainting-like methods to generate returns conditioned on other stock trajectories. The post also addresses the challenge of defining evaluation metrics for synthetic time-series data and contrasts diffusion models with autoregressive models like transformers and GANs, noting the former's avoidance of mode collapse.