mlsanityData to 7 Oct 2026

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

arXiv 2512.15116 ↗First posted 17 Dec 2025cs.LG cs.AIcited by 2

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Abstract (arXiv)

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. Existing Transformer- and diffusion-based imputers achieve strong performance, but they often rely mainly on time-domain modeling and lack adaptive spectral bias for recovering structured temporal gaps. We propose FADTI, a Fourier- and attention-driven diffusion framework for multivariate time series imputation. FADTI introduces a Fourier Bias Projection (FBP) module that injects learnable frequency-aware bias into intermediate hidden states during denoising. It projects intermediate hidden states onto Fourier bases, avoiding direct spectral estimation from masked or zero-filled inputs. With DFT, STFT, and FSST instantiations, FBP captures global periodicity, localized time--frequency variations, and non-stationary oscillatory patterns. By coupling FBP with self-attention and gated convolution, FADTI integrates frequency-domain guidance, temporal dependency modeling, and probabilistic denoising in a unified framework. Experiments on multiple benchmarks, including a new biological imputation benchmark, show that FADTI improves accuracy, uncertainty estimation, and sampling efficiency, especially under high missing rates and structured missing patterns. Code is available at https://github.com/RazeenLI/FADTI

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 40 of its 81 references that are in the map. First posted on arXiv 17 Dec 2025.

Builds on

40 of 81 references.

References · 40

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  5. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  6. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  7. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
  8. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  9. 2022Transformers in Time Series: A Surveycited by 489
  10. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 333
  11. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  12. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 288
28 more
  1. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 198
  2. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 195
  3. 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generationcited by 179
  4. 2019GP-VAE: Deep Probabilistic Time Series Imputationcited by 171
  5. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 167
  6. 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 113
  7. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 93
  8. 2017Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networkscited by 86
  9. 2024TSLANet: Rethinking Transformers for Time Series Representation Learningcited by 71
  10. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
  11. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  12. 2023PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputationcited by 62
  13. 2019NAOMI: Non-Autoregressive Multiresolution Sequence Imputationcited by 59
  14. 2024Deep Learning for Multivariate Time Series Imputation: A Surveycited by 57
  15. 2023ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputationcited by 50
  16. 2020Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Seriescited by 33
  17. 2021Missing Value Imputation on Multidimensional Time Seriescited by 33
  18. 2023Probabilistic Imputation for Time-series Classification with Missing Datacited by 21
  19. 2022Laplacian Convolutional Representation for Traffic Time Series Imputationcited by 18
  20. 2024SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputationcited by 18
  21. 2024MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputationcited by 13
  22. 2024Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputationcited by 11
  23. 2024Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputationcited by 8
  24. 2025LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputationcited by 8
  25. 2023Data Imputation for Sparse Radio Maps in Indoor Positioning (Extended Version)cited by 6
  26. 2023Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation (Extended Version)cited by 6
  27. 2020Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognitioncited by 5
  28. 2024How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputationcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026GAUDI: Geometry-Aware Diffusion for Calibrated Air-Quality Time-Series Imputationcited by 0
  2. 2026Correspondences as Decisions: JevNexus for Decision-Centric Schema Matchingcited by 0