Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation

ArXi:2604.05257v1 Announce Type: cross Diffusion models are increasingly being utilised to create synthetic tabular and time series data for privacy-preserving augmentation. Tabular Denoising Diffusion Probabilistic Models (TabDDPM) generate high-quality synthetic data from heterogeneous tabular datasets but assume independence between samples, limiting their applicability to time-series domains where temporal dependencies are critical. To address this, we propose a temporal extension of Tab.