arXiv Machine Learning

RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation

RDDMPI introduces a residual denoising diffusion model for multivariate time series imputation. By decomposing the missing signal into a baseline reconstruction and a residual uncertainty component, the method conditions the diffusion process on both the completed signal and its latent representation, using a reliability-aware mechanism to balance baseline influence. Experiments on benchmark datasets show that this approach improves reconstruction accuracy and uncertainty quantification compared to prior diffusion-based methods.

arXiv AI

Aug 20

Discretizing Continuous Time Series for Imputation with Masked Diffusion Training

The paper introduces the Masked Diffusion Time-series Imputation Model (MDTIM), which uses a masked diffusion training paradigm to directly predict original values for time series imputation. It separates missing and observed data via a MASK token and employs Stochastic Discretization to convert continuous values into ordinal-aware tokens, preserving temporal dynamics. Experiments on multiple benchmarks show that MDTIM outperforms existing deterministic and generative baselines in robustness and scalability across various missing data scenarios.

By Dongbin Kim, Seungyun Lee, Geonwoo Shin, Jaewook Lee

diffusion benchmarks

arXiv Machine Learning

Jun 5

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.

By Ziwen Kan, Wugeng Zheng, Tianlong Chen, Song Wang

rag diffusion multimodal benchmarks

arXiv Machine Learning

Jun 17

Informative Missingness to Generate Irregular Clinical Time Series

arXiv:2606. 17106v1 Announce Type: new Abstract: Laboratory tests in electronic health records are collected irregularly, and the absence of a test order can be as informative as the measurement itself.

By Hadi Mehdizavareh, Gabriele Santangelo, Giovanna Nicora, Simon Lebech Cichosz, Arianna Dagliati, Arijit Khan, Riccardo Bellazzi

rag diffusion benchmarks

Hugging Face Trending Papers

Jun 3

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness

Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many real-world datasets, however, missingness may arise from two distinct sources: some entries are meaningfully missing (intrinsically absent and semantically valid), while others are missing due to the observation process and should be imputed.

diffusion

arXiv AI

Jun 9

FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation

arXiv:2512. 15116v2 Announce Type: replace-cross Abstract: Multivariate time series imputation is fundamental in applications such as healthcare, traffic forecasting, and biological modeling, where sensor failures and irregular sampling lead to pervasive missing values.

By Runze Li, Hanchen Wang, Wenjie Zhang, Binghao Li, Yu Zhang, Xuemin Lin, Ying Zhang

llms diffusion benchmarks safety

arXiv Machine Learning

Jun 4

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness

arXiv:2606. 05073v1 Announce Type: new Abstract: Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values.

By Lixing Zhang, Yidong Ouyang, Weifu Li, Shixiang Zhu, Guang Cheng, Liyan Xie

diffusion

arXiv Machine Learning

Jun 5

HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputation

arXiv:2606. 05239v1 Announce Type: cross Abstract: Diffusion models have demonstrated strong performance in time series modeling due to their ability to progressively capture complex data distributions through iterative denoising.

By Hongfan Gao, Wangmeng Shen, Bin Yang, Jilin Hu

rag diffusion benchmarks

arXiv Machine Learning

Sep 1

A Deep Latent Variable Framework for Jointly Modeling Missingness, Measurement Error, and Heterogeneity

arXiv:2608.30040v1 Announce Type: cross Abstract: Missing data, measurement error, and population heterogeneity are pervasive challenges in analyzing data arising from modern observational studies an...

By Yasin Khadem Charvadeh, Grace Y. Yi, Mithat G\"onen, Pouya Faroughi

arXiv Machine Learning

Jun 16

Towards a Unified Generative Model for Scarce Time Series with Domain Experts

arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.

By Zihao Yao, Qi Zheng, Jiankai Zuo, Yaying Zhang

llms diffusion fine-tuning

arXiv AI

Jun 3

Latent Laplace Diffusion for Irregular Multivariate Time Series

arXiv:2605. 19805v2 Announce Type: replace-cross Abstract: Irregular multivariate time series impose a trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, while continuous-time models often require sequential solvers prone to drift.

By Zinuo You, Jin Zheng, John Cartlidge

diffusion

arXiv Machine Learning

Jul 8

Temporal Variational Implicit Neural Representations

arXiv:2506. 01544v2 Announce Type: replace Abstract: We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time series that enables efficient and accurate individualized imputation and forecasting.

By Batuhan Koyuncu, Rachael DeVries, Ole Winther, Isabel Valera

fine-tuning

arXiv Machine Learning

Jul 24

Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting

arXiv:2607. 21200v1 Announce Type: cross Abstract: The modeling of hydrometeorological time series with limited observations is a key challenge in the monitoring of hydro-systems and water resources, as well as for flood or drought risk assessment.

By Ferdinand Bhavsar (INRAE), Lionel Benoit (INRAE), Maxime Savatier (ANDRA), Edith Gabriel (INRAE)

llms diffusion efficiency

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