AI RESEARCH
Causal Time Series Generation via Diffusion Models
arXiv CS.LG
•
ArXi:2509.20846v3 Announce Type: replace Time series generation (TSG) synthesizes realistic sequences and has achieved remarkable success. Among TSG, conditional models generate sequences given observed covariates, however, such models learn observational correlations without considering unobserved confounding. In this work, we propose a causal perspective on conditional TSG and