Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
arXiv:2604.05257v1 Announce Type: new 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
arXiv:2604.05257v1 Announce Type: new 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 system produces synthetic time-series that closely resemble real world sensor patterns and achieves comparable classification performance (macro F1-score 0.64, accuracy 0.71). This is especially advantageous for minority class representation and preserving statistical alignment with real distributions. These developments demonstrate that diffusion based models provide effective and adaptable solutions for sequential data synthesis when they are equipped for temporal reasoning. Future work will explore scaling to longer sequences and integrating stronger temporal architectures.
Executive Summary
The article presents a novel extension of Tabular Denoising Diffusion Probabilistic Models (TabDDPM) to address the generation of high-fidelity synthetic time-series data. Recognizing the limitations of TabDDPM in handling temporal dependencies, the authors introduce sequence-aware adaptations, including lightweight temporal adapters and context-aware embedding modules. By reformulating sensor data into windowed sequences and incorporating timestep embeddings, conditional activity labels, and observed/missing masks, the proposed model demonstrates improved temporal realism, diversity, and coherence. Validation on the WISDM accelerometer dataset reveals that synthetic sequences closely mirror real-world patterns while maintaining comparable classification performance (macro F1-score 0.64, accuracy 0.71). The work underscores the potential of diffusion-based models in sequential data synthesis, particularly for enhancing minority class representation and statistical alignment, while outlining future directions for scaling to longer sequences and integrating more sophisticated temporal architectures.
Key Points
- ▸ Proposes a temporal extension of TabDDPM to address limitations in generating time-series data where temporal dependencies are critical.
- ▸ Introduces sequence-aware adaptations, including lightweight temporal adapters and context-aware embedding modules, to model temporal context explicitly.
- ▸ Demonstrates enhanced temporal realism, diversity, and coherence in synthetic time-series data, validated through bigram transition matrices, autocorrelation analysis, and classification performance metrics on the WISDM dataset.
Merits
Innovative Adaptation of Diffusion Models
The paper effectively extends TabDDPM to time-series data by introducing sequence-aware adaptations, addressing a critical gap in the literature where temporal dependencies were previously overlooked in tabular diffusion models.
Enhanced Temporal Realism
The proposed model demonstrates significant improvements in generating temporally coherent synthetic sequences, validated through rigorous statistical analyses such as bigram transition matrices and autocorrelation, which are essential for real-world applications.
Practical Applicability
The model achieves comparable classification performance to real data (macro F1-score 0.64, accuracy 0.71), making it highly relevant for privacy-preserving data augmentation, particularly in domains like healthcare or finance where synthetic data is increasingly used.
Demerits
Limited Sequence Length Handling
The current framework is constrained by its ability to handle only relatively short sequences, as acknowledged by the authors, which may limit its applicability in domains requiring long-term temporal dependencies (e.g., climate modeling or long-term financial forecasting).
Dependence on Preprocessing and Contextual Embeddings
The model's performance is highly dependent on the quality of windowed sequences and the design of context-aware embeddings, which may introduce complexity and potential biases if not carefully curated.
Computational Overhead
While lightweight temporal adapters are introduced, the overall framework may still incur significant computational costs, particularly during training, which could pose challenges for deployment in resource-constrained environments.
Expert Commentary
The authors present a compelling and timely extension of diffusion models to the time-series domain, addressing a significant limitation in existing tabular models. The introduction of temporal adapters and context-aware embeddings is a thoughtful approach to modeling sequential dependencies, and the empirical validation on the WISDM dataset demonstrates the method's practical viability. The focus on temporal realism and diversity is particularly noteworthy, as these aspects are often overlooked in favor of static performance metrics. However, the paper could benefit from further exploration of the model's scalability, particularly in handling longer sequences, and a deeper analysis of the computational trade-offs involved. Additionally, while the classification performance metrics are promising, a broader evaluation across diverse datasets and tasks would strengthen the generalizability claims. Overall, this work is a valuable contribution to the field, bridging the gap between diffusion models and sequential data synthesis with significant potential for real-world impact.
Recommendations
- ✓ Future work should prioritize scaling the model to handle longer sequences, possibly by integrating hierarchical temporal architectures or attention mechanisms to capture long-range dependencies more effectively.
- ✓ The authors should conduct a more comprehensive evaluation across diverse datasets and tasks to validate the model's generalizability and robustness, including comparisons with state-of-the-art time-series generative models.
- ✓ Further research is needed to explore the computational efficiency of the proposed model, particularly in terms of training and inference times, to assess its feasibility for deployment in resource-constrained environments.
Sources
Original: arXiv - cs.LG