[2604.05257] Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation

Computer Science > Machine Learning arXiv:2604.05257 (cs) [Submitted on 6 Apr 2026] Title:Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation Authors:Umang Dobhal, Christina Garcia, Sozo Inoue View a PDF of the paper titled Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation, by Umang Dobhal and 2 other authors View PDF HTML (experimental) Abstract: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 TabDDPM, introducing sequence awareness through the use of lightweight temporal adapters and context-aware embedding modules. By reformulating sensor data into windowed sequences and explicitly modeling temporal context via timestep embeddings, conditional activity labels, and observed/missing masks, our approach enables the generation of temporally coherent synthetic sequences. Compared to baseline and interpolation techniques, validation using bigram transition matrices and autocorrelation analysis shows enhanced temporal realism, diversity, and coherence. On the WISDM accelerometer dataset, the suggested...