# Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking

arXiv: https://arxiv.org/abs/2302.10902 · HTML: https://mlsanity.com/paper/2302.10902/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 78 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 18 of 18)

- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [GP-VAE: Deep Probabilistic Time Series Imputation](https://mlsanity.com/paper/1907.04155/) (2019)
- [MaskGAN: Better Text Generation via Filling in the______](https://mlsanity.com/paper/1801.07736/) (2018)
- [NAOMI: Non-Autoregressive Multiresolution Sequence Imputation](https://mlsanity.com/paper/1901.10946/) (2019)
- [A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series](https://mlsanity.com/paper/2012.00168/) (2020)
- [TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise](https://mlsanity.com/paper/1409.5181/) (2014)
- [Learning from Irregularly-Sampled Time Series: A Missing Data Perspective](https://mlsanity.com/paper/2008.07599/) (2020)
- [Time Series Data Imputation: A Survey on Deep Learning Approaches](https://mlsanity.com/paper/2011.11347/) (2020)
- [Improving Missing Data Imputation with Deep Generative Models](https://mlsanity.com/paper/1902.10666/) (2019)
- [The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms](https://mlsanity.com/paper/2203.06369/) (2022)
- [Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework](https://mlsanity.com/paper/2202.13734/) (2022)
- [Temporal Convolutional Neural Networks for Diagnosis from Lab Tests](https://mlsanity.com/paper/1511.07938/) (2015)
- [Missing Value Imputation Based on Deep Generative Models](https://mlsanity.com/paper/1808.01684/) (2018)
- [Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards](https://mlsanity.com/paper/2110.08949/) (2021)
- [Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project](https://mlsanity.com/paper/2112.03914/) (2021)
- [Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction](https://mlsanity.com/paper/2203.08245/) (2022)

## Built on by (most cited first, 30 of 30)

- [Universal Time-Series Representation Learning: A Survey](https://mlsanity.com/paper/2401.03717/) (2024)
- [Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series](https://mlsanity.com/paper/2506.10412/) (2025)
- [Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking](https://mlsanity.com/paper/2401.04266/) (2024)
- [How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation](https://mlsanity.com/paper/2407.08442/) (2024)
- [The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records](https://mlsanity.com/paper/2603.14003/) (2026)
- [Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation](https://mlsanity.com/paper/2410.01847/) (2024)
- [Machine Learning for Missing Value Imputation](https://mlsanity.com/paper/2410.08308/) (2024)
- [Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation](https://mlsanity.com/paper/2411.04899/) (2024)
- [Using matrix-product states for time-series machine learning](https://mlsanity.com/paper/2412.15826/) (2024)
- [Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series](https://mlsanity.com/paper/2607.04245/) (2026)
- [Imputation Matters: A Deeper Look into an Overlooked Step in Longitudinal Health and Behavior Sensing Research](https://mlsanity.com/paper/2412.06018/) (2024)
- [DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive Framework](https://mlsanity.com/paper/2501.10910/) (2025)
- [A Proposed Paradigm for Imputing Missing Multi-Sensor Data in the Healthcare Domain](https://mlsanity.com/paper/2601.03565/) (2026)
- [MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data](https://mlsanity.com/paper/2605.22775/) (2026)
- [Machine Learning Based Missing Values Imputation in Categorical Datasets](https://mlsanity.com/paper/2306.06338/) (2023)
- [Data-Driven Ergonomic Risk Assessment of Complex Hand-intensive Manufacturing Processes](https://mlsanity.com/paper/2403.05591/) (2024)
- [Imputation of Missing Data in Smooth Pursuit Eye Movements Using a Self-Attention-based Deep Learning Approach](https://mlsanity.com/paper/2506.00545/) (2025)
- [STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems](https://mlsanity.com/paper/2508.19011/) (2025)
- [MAGIC: Multi-task Gaussian process for joint imputation and classification in healthcare time series](https://mlsanity.com/paper/2509.19577/) (2025)
- [A Visual Diagnostics Framework for District Heating Data: Enhancing Data Quality for AI-Driven Heat Consumption Prediction](https://mlsanity.com/paper/2510.00872/) (2025)
- [Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification](https://mlsanity.com/paper/2510.02216/) (2025)
- [Ocular-Induced Abnormal Head Posture: Diagnosis and Missing Data Imputation](https://mlsanity.com/paper/2510.05649/) (2025)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)
- [End-to-End Learning for Partially-Observed Time Series with PyPOTS](https://mlsanity.com/paper/2604.24041/) (2026)
- [Informative Missingness to Generate Irregular Clinical Time Series](https://mlsanity.com/paper/2606.17106/) (2026)
- [Evaluating Deep Multivariate Imputation Models on Wearable Device Data](https://mlsanity.com/paper/2608.24436/) (2026)
- [Predicting Future Organ Dysfunction in ICU Patients Using Temporal Convolutional Networks on MIMIC-IV Data](https://mlsanity.com/paper/2608.29301/) (2026)
- [SMOTE-VAR: An Uncertainty-Aware Oversampling Method for Predicting Depression Remission in University Students](https://mlsanity.com/paper/2608.30102/) (2026)
- [Predicting Symptoms of Amotivation and Anhedonia among University Students with a Novel Oversampling Method](https://mlsanity.com/paper/2609.29690/) (2026)
- [The Effect of Missingness-Pattern Mismatch on Method Selection for Time-Series Classification: A Controlled Empirical Study](https://mlsanity.com/paper/2610.05368/) (2026)

## Abstract (arXiv)

The imputation of missing values in multivariate time series (MTS) data is critical in ensuring data quality and producing reliable data-driven predictive models. Apart from many statistical approaches, a few recent studies have proposed state-of-the-art deep learning methods to impute missing values in MTS data. However, the evaluation of these deep methods is limited to one or two data sets, low missing rates, and completely random missing value types. This survey performs six data-centric experiments to benchmark state-of-the-art deep imputation methods on five time series health data sets. Our extensive analysis reveals that no single imputation method outperforms the others on all five data sets. The imputation performance depends on data types, individual variable statistics, missing value rates, and types. Deep learning methods that jointly perform cross-sectional (across variables) and longitudinal (across time) imputations of missing values in time series data yield statistically better data quality than traditional imputation methods. Although computationally expensive, deep learning methods are practical given the current availability of high-performance computing resources, especially when data quality and sample size are highly important in healthcare informatics. Our findings highlight the importance of data-centric selection of imputation methods to optimize data-driven predictive models.