Can you use autoregressive diffusion to generate market data?

This post introduces a novel approach to generating market data using autoregressive diffusion models. It details the technical challenges of modeling market data's mixed continuous and discrete nature, comparing DDPM and flow matching, and proposing 'atom smoothing' and fractured categorical predictions to handle discontinuities. The contribution lies in adapting diffusion models for financial time-series generation and exploring specific techniques to address the jaggedness of market data.