mlsanityData to 7 Oct 2026

Recurrent Neural Networks for Multivariate Time Series with Missing Values

arXiv 1606.01865 ↗First posted 6 Jun 2016cs.LG cs.NE stat.MLcited by 548

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Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provides useful insights for better understanding and utilization of missing values in time series analysis.

Key facts. Cited by 548 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2016.

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Citing papers per year: 2021: 70; 2022: 69; 2023: 82; 2024: 89; 2025: 107; 2026: 131.

548 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2021Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layerscited by 607
  3. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
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  5. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 195
  6. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  7. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 167
  8. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
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  10. 2021CKConv: Continuous Kernel Convolution For Sequential Datacited by 117
  11. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  12. 2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 110
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  2. 2021Neural Flows: Efficient Alternative to Neural ODEscited by 99
  3. 2021Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Seriescited by 96
  4. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 93
  5. 2023DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Modelscited by 81
  6. 2023Time Series as Images: Vision Transformer for Irregularly Sampled Time Seriescited by 79
  7. 2021CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction taskscited by 75
  8. 2023MOTOR: A Time-To-Event Foundation Model For Structured Medical Recordscited by 75
  9. 2023Diffusion Models for Time Series Applications: A Surveycited by 72
  10. 2021Time Series Prediction using Deep Learning Methods in Healthcarecited by 71
  11. 2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusioncited by 67
  12. 2021Closed-form Continuous-time Neural Modelscited by 65
  13. 2024Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysiscited by 65
  14. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  15. 2023PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputationcited by 62
  16. 2024ContiFormer: Continuous-Time Transformer for Irregular Time Series Modelingcited by 61
  17. 2024Deep Learning for Multivariate Time Series Imputation: A Surveycited by 57
  18. 2023Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Seriescited by 54
  19. 2025Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baselinecited by 54
  20. 2021S-LIME: Stabilized-LIME for Model Explanationcited by 52
  21. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  22. 2023ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputationcited by 50
  23. 2023Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEscited by 48
  24. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  25. 2023Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Predictioncited by 47
  26. 2021Neural Controlled Differential Equations for Online Prediction Taskscited by 46
  27. 2022Transformers in Time-series Analysis: A Tutorialcited by 45
  28. 2022GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networkscited by 43
  29. 2021Neural Point Process for Learning Spatiotemporal Event Dynamicscited by 42
  30. 2023CUTS+: High-dimensional Causal Discovery from Irregular Time-seriescited by 39
  31. 2024Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRscited by 39
  32. 2022Contrastive Learning for Unsupervised Domain Adaptation of Time Seriescited by 36
  33. 2023GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputationcited by 35
  34. 2024Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Datacited by 35
  35. 2021Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologiescited by 34
  36. 2022Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modelingcited by 33
  37. 2021MUFASA: Multimodal Fusion Architecture Search for Electronic Health Recordscited by 30
  38. 2021Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Seriescited by 30

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