arxiv: 2604.05257 · v1 · submitted 2026-04-06 · 💻 cs.LG · cs.AI

Recognition: no theorem link

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

Christina Garcia, Sozo Inoue, Umang Dobhal

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:39 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords diffusion modelstime series generationtabular datasynthetic sensor datatemporal coherencedata augmentationactivity recognition

0 comments

The pith

Tabular diffusion models can be extended to generate temporally coherent time-series by adding lightweight adapters and context embeddings for sequences.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to show that a model originally designed for independent tabular samples can handle time-series data by reformulating inputs as windowed sequences and injecting temporal context. This matters because sensor data often requires preserving dependencies across time for tasks like activity recognition while enabling privacy-safe synthetic augmentation. The extension uses adapters and embeddings for timesteps, labels, and masks to produce sequences that align statistically with real distributions. Validation relies on transition matrices, autocorrelation checks, and downstream classification performance that matches real data patterns, especially for underrepresented classes.

Core claim

By reformulating sensor data into windowed sequences and explicitly modeling temporal context via timestep embeddings, conditional activity labels, and observed/missing masks, the temporal extension of TabDDPM generates synthetic sequences with improved coherence and realism over baselines and interpolation methods.

What carries the argument

lightweight temporal adapters and context-aware embedding modules that inject sequence awareness into the denoising process

If this is right

Synthetic time-series preserve statistical properties of real sensor data and support minority class representation in downstream tasks.
The generated sequences achieve temporal realism measurable through transition matrices and autocorrelation analysis.
Classification on the synthetic data yields performance close to that obtained from real data.
The approach provides a flexible way to augment sequential datasets while maintaining alignment with original distributions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same adapter approach could transfer to other domains with sequential structure such as financial or environmental time series.
Replacing lightweight adapters with dedicated recurrent or attention-based temporal modules might further strengthen long-range dependency modeling.
Explicit handling of missing values via masks suggests the method could extend to irregular sampling patterns common in real sensor streams.

Load-bearing premise

That fixed window sizes and the added embeddings capture the necessary temporal dependencies without creating artifacts in the generated sequences.

What would settle it

Direct comparison of autocorrelation functions and bigram transition probabilities between generated and real sequences on data windows longer than those used in training.

Figures

Figures reproduced from arXiv: 2604.05257 by Christina Garcia, Sozo Inoue, Umang Dobhal.

**Figure 1.** Figure 1: TabDDPM architecture for tabular data with quantiletransformed numerical and one-hot encoded categorical features. Additionally, TabDDPM lacks contextual conditioning methods, which makes it unable to include outside data like activity type or user-specific context. Its robustness in practical sensor contexts, where signal loss or irregular sampling is widespread, is limited by the lack of missing data ha… view at source ↗

**Figure 2.** Figure 2: Proposed Temporal TabDDPM architecture with context-aware embeddings and temporal adapters. one-hot encoded for categorical attributes, replicating the TabDDPM preprocessing. These per feature encodings are concatenated into x0 ∈ R B×T ×D. Before entering the forward diffusion block, three additional modules – Timestep Embedding, Conditional Embedding, and Observed/Missing Mask – are merged with x0 to fo… view at source ↗

**Figure 3.** Figure 3: Activity distribution across the five selected users. An 80/20 stratified split was applied to create training and test sets, ensuring proportional representation of all activity classes. The held out test set was used exclusively for evaluating both the generative model and downstream classification performance. 3.3. Data Preprocessing. Raw accelerometer data were segmented into overlapping windows of 10… view at source ↗

**Figure 4.** Figure 4: Training and validation loss over 50 epochs. 3.4.3. Implementation Environment. All experiments were run on an Apple M2 GPU using the MPS backend. The implementation follows the modular structure of TabDDPM [1] with the temporal and contextual extensions described in Section 3. 3.5. Synthetic Data Generation. Following convergence, synthetic sequences were generated using the reverse diffusion process. Sta… view at source ↗

**Figure 5.** Figure 5: compares accelerometer sequences for the Downstairs activity across real data, the Original TabDDPM, and the proposed method. Proposed TabDDPM produces smoother trajectories with consistent temporal transitions, avoiding the irregular spikes present in the original TabDDPM and demonstrating the benefit of explicit temporal modeling. 0 10 20 30 40 50 Time step (20Hz) 20 10 0 10 20 Sensor value Original Down… view at source ↗

**Figure 6.** Figure 6: Distribution comparison of accelerometer features (x, y, z) between real and synthetic data. These findings show that proposed TabDDPM effectively captures latent human motion dynamics and generates realistic, label conditioned synthetic sequences suitable for data augmentation and balancing. 4.2. Balancing and Data Augmentation. The WISDM dataset shows substantial class imbalance, with locomotive activi… view at source ↗

**Figure 7.** Figure 7: Class distribution before and after augmentation. 4.3. Classification Evaluation. To evaluate the effect of generative augmentation on downstream classification, Random Forest classifiers were trained on four versions of the training data: (a) the original unbalanced dataset, (b) the SMOTE balanced dataset, (c) the dataset augmented with synthetic sequences from the Original TabDDPM, and (d) the dataset a… view at source ↗

**Figure 8.** Figure 8: Confusion matrices for Random Forest classifiers trained on (a) Original, (b) SMOTE augmented, (c) Original TabDDPM, and (d) Proposed TabDDPM datasets. The Original TabDDPM exhibited reduced precision and recall for static activities (Sitting: 0.88, Standing: 0.81), reflecting difficulty in generating consistent low variance sequences. Proposed TabDDPM improved these metrics substantially (Sitting: 0.99,… view at source ↗

**Figure 9.** Figure 9: Bigram transition matrices for the X-axis showing short-range temporal transitions for (a) Original data, (b) Proposed TabDDPM, (c) SMOTE, and (d) Original TabDDPM. Original TabDDPM, and Proposed TabDDPM, reflecting short range sensor transitions. Proposed TabDDPM replicates strong transitions seen in real data, while SMOTE and Original TabDDPM yield flatter, less structured patterns. 0.25 0.00 0.25 0.50… view at source ↗

**Figure 10.** Figure 10: Autocorrelation functions (ACF) for Sitting and Upstairs activities on the Z-axis, comparing temporal dependence across methods [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper extends TabDDPM for time-series generation by reformulating sensor data into windowed sequences and introducing lightweight temporal adapters plus context-aware embedding modules that incorporate timestep embeddings, conditional activity labels, and observed/missing masks. This is claimed to produce temporally coherent synthetic sequences. On the WISDM accelerometer dataset, the approach is reported to outperform baselines and interpolation techniques on bigram transition matrices and autocorrelation, while yielding downstream classification performance of macro F1-score 0.64 and accuracy 0.71, with benefits for minority classes and statistical alignment.

Significance. If the attribution to the temporal extensions holds, the work provides a practical route to synthetic time-series data that preserves temporal structure for privacy-preserving augmentation in sensor-based applications. The concrete downstream metrics and use of bigram/autocorrelation comparisons are positive elements that allow direct evaluation of realism and utility.

major comments (2)

The central claim that the lightweight temporal adapters, timestep embeddings, conditional labels, and masks produce measurably better temporal coherence than a non-temporal TabDDPM baseline rests on comparisons to unspecified 'baseline and interpolation techniques.' No ablation is presented that applies the base TabDDPM to the identical windowed sequences while removing the added temporal components; without this, the reported bigram, autocorrelation, and F1/accuracy gains cannot be attributed specifically to the proposed machinery rather than to windowing alone.
The experimental validation reports macro F1 0.64 and accuracy 0.71 but provides no details on baseline implementations, whether data splits and hyperparameters were fixed prior to seeing results, or statistical significance testing. This directly affects the soundness of the claim that the generated sequences achieve 'comparable classification performance' and 'enhanced temporal realism.'

minor comments (1)

The abstract states that the system 'achieves comparable classification performance' yet the manuscript should clarify the exact classifier architecture, training protocol, and whether the same downstream model was used for both real and synthetic data.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address each major comment point by point below, agreeing where the manuscript requires strengthening and outlining the specific changes we will make.

read point-by-point responses

Referee: The central claim that the lightweight temporal adapters, timestep embeddings, conditional labels, and masks produce measurably better temporal coherence than a non-temporal TabDDPM baseline rests on comparisons to unspecified 'baseline and interpolation techniques.' No ablation is presented that applies the base TabDDPM to the identical windowed sequences while removing the added temporal components; without this, the reported bigram, autocorrelation, and F1/accuracy gains cannot be attributed specifically to the proposed machinery rather than to windowing alone.

Authors: We agree that the absence of a direct ablation isolating the temporal adapters from the windowing step limits the strength of attribution. The manuscript presents comparisons against baseline and interpolation techniques applied to the time-series data, but does not explicitly apply the unmodified TabDDPM to the same windowed sequences (e.g., by flattening windows into tabular samples). To address this, we will add a new ablation study in the revised manuscript that applies the base TabDDPM to the identical windowed WISDM sequences without the temporal adapters, timestep embeddings, conditional labels, or masks, and report the resulting bigram, autocorrelation, and downstream metrics for direct comparison. revision: yes
Referee: The experimental validation reports macro F1 0.64 and accuracy 0.71 but provides no details on baseline implementations, whether data splits and hyperparameters were fixed prior to seeing results, or statistical significance testing. This directly affects the soundness of the claim that the generated sequences achieve 'comparable classification performance' and 'enhanced temporal realism.'

Authors: We acknowledge that the experimental section is insufficiently detailed on these points. The manuscript reports the macro F1 and accuracy figures but does not describe baseline implementations, confirm that splits and hyperparameters were fixed before evaluation, or include statistical significance tests. In the revision we will expand the experimental section to provide: full implementation details and hyperparameters for all baselines; an explicit statement that data splits and hyperparameters were determined prior to generating or evaluating synthetic data; and results of statistical significance testing (e.g., paired t-tests across multiple random seeds) for the reported F1, accuracy, bigram, and autocorrelation metrics. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical extension validated against held-out data

full rationale

The paper presents a methodological extension of TabDDPM via windowing, lightweight adapters, timestep/conditional/mask embeddings, and reports direct empirical comparisons (bigram transitions, autocorrelation, downstream F1/accuracy) to baselines on the WISDM dataset. No load-bearing step defines a quantity in terms of itself, renames a fit as a prediction, or relies on a self-citation chain whose cited result is unverified; all performance numbers are measured against external held-out real sequences rather than being forced by construction from the model's own parameters.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

The central claim rests on the assumption that standard diffusion training can be made temporally aware by adding two new architectural modules whose effectiveness is demonstrated only empirically on one dataset; no new physical entities are postulated.

free parameters (1)

sequence window length
Chosen when reformulating sensor streams into fixed windows; value not stated in abstract but required for the method to function.

axioms (1)

domain assumption Diffusion models trained on independent samples can be extended to capture temporal dependencies by adding lightweight adapters and context embeddings
Invoked when the authors state that the reformulation and new modules enable coherent sequence generation.

invented entities (2)

lightweight temporal adapters no independent evidence
purpose: Introduce sequence awareness into the TabDDPM backbone
New component proposed to overcome the independence assumption of the base model.
context-aware embedding modules no independent evidence
purpose: Incorporate timestep, label, and mask information for temporal context
New modules introduced to condition the diffusion process on sequence position and missingness.

pith-pipeline@v0.9.0 · 5524 in / 1531 out tokens · 52482 ms · 2026-05-10T18:39:25.959835+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

21 extracted references · 17 canonical work pages · 1 internal anchor

[1]

”TabDDPM: Modelling Tab- ular Data with Diffusion Models”.https://arxiv.org/abs/2209.15421

Kotelnikov, A., Baranchuk, D., Rubachev, I., and Babenko, A. ”TabDDPM: Modelling Tab- ular Data with Diffusion Models”.https://arxiv.org/abs/2209.15421

work page arXiv
[2]

Gorishniy, I

Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A. ”Revisiting Deep Learning Models for Tabular Data”.https://arxiv.org/abs/2106.11959

work page arXiv
[3]

Mixed-type tabular data synthesis with score-based diffusion in latent space.arXiv preprint arXiv:2310.09656, 2023

Zhang, H., Zhang, J., Srinivasan, B., Shen, Z., Qin, X., Faloutsos, C., Rangwala, H., and Karypis, G. ”Mixed-Type Tabular Data Synthesis with Score-Based Diffusion in Latent Space”.https://arxiv.org/abs/2310.09656

work page arXiv
[4]

”TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation”.https://arxiv.org/abs/2407.04211

Qian, J., Sun, M., Zhou, S., Wan, B., Li, M., and Chiang, P. ”TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation”.https://arxiv.org/abs/2407.04211

work page arXiv
[5]

M., and Zhang, C

Ma, C., Tschiatschek, S., Turner, R., Hern´ andez-Lobato, J. M., and Zhang, C. ”VAEM: A Deep Generative Model for Heterogeneous Mixed Type Data”.https://arxiv.org/abs/ 2006.11941

work page arXiv 2006
[6]

Diffusion-ts: Interpretable diffusion for general time series generation.arXiv preprint arXiv:2403.01742, 2024

Yuan, X. and Qiao, Y. ”Diffusion-TS: Interpretable Diffusion for General Time Series Gen- eration”.https://arxiv.org/abs/2403.01742

work page arXiv
[7]

”TimeAutoDiff: Combining Autoencoder and Diffusion Model for Time Series Tabular Data Synthesizing”

Suh, N., Yang, Y., Hsieh, D., Luan, Q., Xu, S., Zhu, S., and Cheng, G. ”TimeAutoDiff: Combining Autoencoder and Diffusion Model for Time Series Tabular Data Synthesizing”. https://arxiv.org/abs/2406.16028

work page arXiv
[8]

”Time-series Generative Adver- sarial Networks”.https://proceedings.neurips.cc/paper_files/paper/2019/file/ c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf

Yoon, J., Jarrett, D., and van der Schaar, M. ”Time-series Generative Adver- sarial Networks”.https://proceedings.neurips.cc/paper_files/paper/2019/file/ c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf

2019
[9]

”Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees”.https://arxiv.org/abs/2309

Jolicoeur-Martineau, A., Fatras, K., and Kachman, T. ”Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees”.https://arxiv.org/abs/2309. 09968
[10]

”SoK: Behind the Accuracy of Complex Human Activity Recog- nition Using Deep Learning”

Nguyen, D., and Le-Khac, N. ”SoK: Behind the Accuracy of Complex Human Activity Recog- nition Using Deep Learning”. arXiv preprint arXiv:2405.00712, 2024.https://arxiv.org/ abs/2405.00712

work page arXiv 2024
[11]

”CAPTURE-24: A Large Dataset of Wrist-Worn Activity Tracker Data Collected in the Wild for Human Activity Recognition”

Chan, S., Hang, Y., Tong, C., Acquah, A., Schonfeldt, A., Gershuny, J., and Doherty, A. ”CAPTURE-24: A Large Dataset of Wrist-Worn Activity Tracker Data Collected in the Wild for Human Activity Recognition”. Scientific Data, vol. 11, no. 1135, 2024.https://doi.org/ 10.1038/s41597-024-03960-3

work page doi:10.1038/s41597-024-03960-3 2024
[12]

H., Bilal, M., and Gani, A

Arshad, M. H., Bilal, M., and Gani, A. ”Human Activity Recognition: Review, Taxonomy and Open Challenges”. Sensors, vol. 22, no. 17, 6463, 2022.https://doi.org/10.3390/s22176463

work page doi:10.3390/s22176463 2022
[13]

R., Weiss, G

Kwapisz, J. R., Weiss, G. M., and Moore, S. A. ”Activity Recognition using Cell Phone Ac- celerometers”.https://www.cis.fordham.edu/wisdm/includes/files/sensorKDD-2010.pdf 16 UMANG DOBHAL, CHRISTINA GARCIA, AND SOZO INOUE

2010
[14]

Xaviar, S., Hang, Y., Tong, C., et al. (2023). Robust Multimodal Fusion for Human Activity Recognition.https://arxiv.org/abs/2303.04636

work page arXiv 2023
[15]

Parameter- Efficient Transfer Learning for NLP .arXiv2019, arXiv:1902.00751

Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S. ”Parameter-Efficient Transfer Learning for NLP”. In Proceedings of the International Conference on Learning Representations (ICLR), 2019.https://arxiv. org/abs/1902.00751

work page arXiv 2019
[16]

arXiv preprint arXiv:2005.00247 , year=

Pfeiffer, J., Kamath, A., R¨ uckl´ e, A., Cho, K., and Gurevych, I. ”AdapterFusion: Non- Destructive Task Composition for Transfer Learning”. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021. https://arxiv.org/abs/2005.00247

work page arXiv 2021
[17]

”Diffusion Adapters: Efficient Fine- Tuning of Diffusion Models”

Ruoss, A., Wertheimer, D., Chen, J., and Engel, J. ”Diffusion Adapters: Efficient Fine- Tuning of Diffusion Models”. arXiv preprint arXiv:2308.05599, 2023.https://arxiv.org/ abs/2308.05599

work page arXiv 2023
[18]

Denoising Diffusion Probabilistic Models

Ho, J., Jain, A., and Abbeel, P. ”Denoising Diffusion Probabilistic Models.” Advances in Neu- ral Information Processing Systems (NeurIPS), 2020.https://arxiv.org/abs/2006.11239

work page internal anchor Pith review arXiv 2020
[19]

Improved denois- ing diffusion probabilistic models.arXiv preprint arXiv:2102.09672,

Nichol, A., and Dhariwal, P. ”Improved Denoising Diffusion Probabilistic Models.” Proceed- ings of the International Conference on Machine Learning (ICML), 2021.https://arxiv. org/abs/2102.09672

work page arXiv 2021
[20]

Dobhal, U., Garcia, C., and Inoue, S. ”Synthetic Skeleton Data Generation using Large Lan- guage Model for Nurse Activity Recognition.” Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’24), pages 493–499. Association for Computing Machinery, New York, NY, USA, 2024.https://doi.org/10. 1145/3675094.3678445

work page arXiv 2024
[21]

Dobhal, U., Garcia, C., and Inoue, S. ”Sample Selection Strategy for Synthetic Data Gener- ation on Gesture Phase Recognition.” Proceedings of the International Conference on Ubiq- uitous Computing and Ambient Intelligence (UCAmI), 2025. Kyushu Institute of Technology, Fukuoka, Japan Email address:dobhal-umang638@mail.kyutech.jp Email address:alvarez7.chr...

2025