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Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models

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arxiv 2208.09399 v3 pith:5K2VLABP submitted 2022-08-19 cs.LG stat.ML

Juan Miguel Lopez Alcaraz , Nils Strodthoff This is my paper · ORCID
classification cs.LGstat.ML
keywords dataimputationmodelsseriestimeforecastingmodelscenarios
verification ladder T0 review T1 audit T2 compute T3 formal
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The imputation of missing values represents a significant obstacle for many real-world data analysis pipelines. Here, we focus on time series data and put forward SSSD, an imputation model that relies on two emerging technologies, (conditional) diffusion models as state-of-the-art generative models and structured state space models as internal model architecture, which are particularly suited to capture long-term dependencies in time series data. We demonstrate that SSSD matches or even exceeds state-of-the-art probabilistic imputation and forecasting performance on a broad range of data sets and different missingness scenarios, including the challenging blackout-missing scenarios, where prior approaches failed to provide meaningful results.

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    On an 8-month Italian hospital EMF dataset, a working-hour-conditioned multivariate diffusion forecaster beats the best baseline in CRPS and NRMSE, though the headline percentages do not match the paper's own aggregate table.

  2. MBDiff: Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation

    cs.LG 2026-07 conditional novelty 6.0 of 10

    A multi-view behavior-aware conditional diffusion model for imputing missing utility-meter data is claimed to beat ten baselines on a Florida utility dataset, though the paper's own tables conflict with parts of the claim.

  3. A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention

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  4. Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting

    cs.AI 2026-06 conditional novelty 6.0 of 10

    DiffDiff rewires diffusion forecasting so corruption gradually emphasizes second-order differences, concentrating generation on history-uncertain parts and improving forecasts on seven benchmarks.

  5. DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting

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    DropoutTS uses spectral reconstruction residuals to set per-sample dropout rates, reporting consistent robustness gains on six forecasting backbones without architectural changes.

  6. Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Pre-trained multivariate time-series imputation models frequently return values that violate known relations between variables, and a diffusion-based score can detect and filter these errors.

  7. Knowledge-guided Complex Diffusion Model for PolSAR Image Classification in Contourlet Domain

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    A knowledge-guided complex diffusion model in the contourlet domain is reported to outperform five baselines on three PolSAR classification datasets.

  8. Cross-Domain Conditional Diffusion Models for Time Series Imputation

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A diffusion-based framework with frequency mixup and selective consistency alignment improves cross-domain time series imputation.

  9. RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting

    cs.LG 2025-09 conditional novelty 5.0 of 10

    RDIT adds residual diffusion and variance calibration on top of a strong point forecaster, achieving best CRPS on seven of eight datasets and lower PICP distance in most settings.

  10. Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching

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    CGFM uses an auxiliary model's predictions as the source for flow matching to learn forecast residuals and improve time series forecasts.

  11. Diffusion Models for Time Series Forecasting: A Survey

    stat.ML 2025-07 conditional novelty 4.0 of 10

    A survey classifies diffusion-based time series forecasting models into a two-axis taxonomy by conditioning source and integration method.

  12. Imputation of Missing Data in Smooth Pursuit Eye Movements Using a Self-Attention-based Deep Learning Approach

    cs.LG 2025-05 conditional novelty 4.0 of 10

    A self-attention imputation model combined with a convolutional autoencoder refinement fills missing segments in smooth pursuit eye movements more accurately than PCHIP, SSA, and KNN, especially for long gaps.

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