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Deep Learning for Multivariate Time Series Imputation: A Survey
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Missing values are ubiquitous in multivariate time series (MTS) data, posing significant challenges for accurate analysis and downstream applications. In recent years, deep learning-based methods have successfully handled missing data by leveraging complex temporal dependencies and learned data distributions. In this survey, we provide a comprehensive summary of deep learning approaches for multivariate time series imputation (MTSI) tasks. We propose a novel taxonomy that categorizes existing methods based on two key perspectives: imputation uncertainty and neural network architecture. Furthermore, we summarize existing MTSI toolkits with a particular emphasis on the PyPOTS Ecosystem, which provides an integrated and standardized foundation for MTSI research. Finally, we discuss key challenges and future research directions, which give insight for further MTSI research. This survey aims to serve as a valuable resource for researchers and practitioners in the field of time series analysis and missing data imputation tasks.A well-maintained MTSI paper and tool list are available at https://github.com/WenjieDu/Awesome_Imputation.
Forward citations
Cited by 7 Pith papers
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MBDiff: Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation
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.
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Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
A location-scale VAE with a GRU volatility path that transfers and evolves scale from look-back to horizon yields better CRPS/NMAE than strong probabilistic and point baselines on nine datasets.
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Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models
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.
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Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning
A joint sequence-image framework with contrastive and clustering losses improves classification of irregular medical time series with heavy missingness.
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NeuralPrefix: A Zero-shot Sensory Data Imputation Plugin
A Neural ODE-based prefix model trained on one sensory modality imputes 50 percent missing frames in unseen modalities with SSIM around 0.88 to 0.94, without retraining.
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FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
FADTI couples a learnable Fourier-bias projection (DFT/STFT/synchrosqueezed variants) with conditional diffusion and attention or gated convolution, reporting best MAE in 10 of 12 settings and a new yeast-cell imputat...
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Imputation of Missing Data in Smooth Pursuit Eye Movements Using a Self-Attention-based Deep Learning Approach
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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