mlsanityData to 14 Oct 2026

SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation

arXiv 2410.13338 ↗First posted 17 Oct 2024cs.LG cs.AIcited by 18

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Probabilistic time series imputation has been widely applied in real-world scenarios due to its ability for uncertainty estimation and denoising diffusion probabilistic models~(DDPMs) have achieved great success in probabilistic time series imputation tasks with its power to model complex distributions. However, current DDPM-based probabilistic time series imputation methodologies are confronted with two types of challenges: 1)textitThe backbone modules of the denoising parts are not capable of achieving sequence modeling with low time complexity. 2)~textitThe architecture of denoising modules can not handle the dependencies in the time series data effectively. To address the first challenge, we explore the potential of state space model, namely Mamba, as the backbone denoising module for DDPMs. To tackle the second challenge, we carefully devise several SSM-based blocks for time series data modeling. Experimental results demonstrate that our approach can achieve state-of-the-art time series imputation results on multiple real-world datasets. Our datasets and code are available at hrefhttps://github.com/decisionintelligence/SSD-TS/https://github.com/decisionintelligence/SSD-TS/

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 40 of its 83 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

40 of 83 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation7 Jul

Other references · 39

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  6. 2013Auto-Encoding Variational Bayescited by 4,053
  7. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  8. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  9. 2016Density estimation using Real NVPcited by 2,042
  10. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  11. 2019Root Mean Square Layer Normalizationcited by 1,480
  12. 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,336
27 more
  1. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 605
  2. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  3. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 508
  4. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 290
  5. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 249
  6. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 198
  7. 2019GP-VAE: Deep Probabilistic Time Series Imputationcited by 172
  8. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 169
  9. 2020Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flowscited by 144
  10. 2020Neural Rough Differential Equations for Long Time Seriescited by 143
  11. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 142
  12. 2024Is Mamba Effective for Time Series Forecasting?cited by 114
  13. 2024CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patchingcited by 94
  14. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  15. 2022Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Versioncited by 59
  16. 2023ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputationcited by 50
  17. 2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encodercited by 39
  18. 2024A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Datacited by 39
  19. 2020Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Seriescited by 33
  20. 2023Temporal and Heterogeneous Graph Neural Network for Financial Time Series Predictioncited by 30
  21. 2023Provably Convergent Schr"odinger Bridge with Applications to Probabilistic Time Series Imputationcited by 27
  22. 2023Probabilistic Imputation for Time-series Classification with Missing Datacited by 22
  23. 2021Causal Hidden Markov Model for Time Series Disease Forecastingcited by 19
  24. 2020Time series forecasting with Gaussian Processes needs priorscited by 13
  25. 2024Zero-shot Imputation with Foundation Inference Models for Dynamical Systemscited by 13
  26. 2022Long-Term Missing Value Imputation for Time Series Data Using Deep Neural Networkscited by 7
  27. 2023Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Seriescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 11; 2026: 7.

18 papers.

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  1. 2025Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baselinecited by 55
  2. 2025Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Controlcited by 28
  3. 2026Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecastingcited by 9
  4. 2025K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecastingcited by 8
  5. 2025Cross-Domain Conditional Diffusion Models for Time Series Imputationcited by 4
  6. 2025Task-Aware Mixture-of-Experts for Time Series Analysiscited by 2
  7. 2025FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputationcited by 2
  8. 2026Net-Ev2: A Generative Simulator for Network Event Evolutioncited by 1
  9. 2025Segregation and Context Aggregation Network for Real-time Cloud Segmentationcited by 0
  10. 2025FADE: Adversarial Concept Erasure in Flow Modelscited by 0
  11. 2025GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Valuescited by 0
  12. 2025Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustnesscited by 0
6 more
  1. 2025PSMamba: Progressive Self-supervised Vision Mamba for Plant Disease Recognitioncited by 0
  2. 2026TimeGMM: Single-Pass Probabilistic Forecasting via Adaptive Gaussian Mixture Models with Reversible Normalizationcited by 0
  3. 2026Deep Time-Series Forecasting in 10 Years: A Surveycited by 0
  4. 2026Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecastingcited by 0
  5. 2026TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolutioncited by 0
  6. 2026Disentangling Multi-View Scanning in Mamba for Network Traffic Anomaly Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
  2. 2024Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation
  3. 2026Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective
  4. 2025Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification
  5. 2026Discretizing Continuous Time Series for Imputation with Masked Diffusion Training
  6. 2024TIMBA: Time series Imputation with Bi-directional Mamba Blocks and Diffusion models
  7. 2025DiM-TS: Bridge the Gap between Selective State Space Models and Time Series for Generative Modeling
  8. 2024MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation