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

📰 ArXiv cs.AI

Extending Tabular Denoising Diffusion Probabilistic Models for time-series data generation with temporal dependencies

advanced Published 8 Apr 2026

Action Steps

Understand the limitations of Tabular Denoising Diffusion Probabilistic Models (TabDDPM) in time-series domains
Propose a temporal extension to TabDDPM to account for temporal dependencies
Implement and evaluate the extended model for time-series data generation
Apply the extended model to real-world datasets for privacy-preserving data augmentation

Who Needs to Know This

Data scientists and AI engineers working on time-series data generation can benefit from this research to improve synthetic data quality, and product managers can apply this to enhance data privacy and augmentation strategies

Key Insight

💡 Accounting for temporal dependencies is crucial for generating high-quality synthetic time-series data

Key Takeaways

Extending Tabular Denoising Diffusion Probabilistic Models for time-series data generation with temporal dependencies

Full Article

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

Abstract:
arXiv:2604.05257v1 Announce Type: cross 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

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