Awesome_Imputation
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WenjieDu
Awesome Deep Learning for Time-Series Imputation, including a must-read paper list about applying neural networks to impute incomplete time series containing NaN missing values/data
The open-resource repository for the paper Deep Learning for Multivariate Time Series Imputation: A Survey
from 
PyPOTS Research.
The code and configurations for reproducing the experimental results in the paper are available under
the folder time_series_imputation_survey_code.
If you find this repository helpful to your work, please kindly star it and cite our survey paper (author profile links: Jun Wang, Wenjie Du, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, Qingsong Wen) as follows:
@article{wang2024deep,
title={Deep Learning for Multivariate Time Series Imputation: A Survey},
author={Wang, Jun and Du, Wenjie and Cao, Wei and Zhang, Keli and Wang, Wenjia and Liang, Yuxuan and Wen, Qingsong},
journal={arXiv preprint arXiv:2402.04059},
year={2024}
}
🤗 Contributions to update new resources and articles are very welcome!
❖ Time-Series Imputation Toolkits
Datasets
TSDB (Time Series Data Beans): a Python toolkit can load 169 public time-series datasets with a single line of code.
Missingness
PyGrinder: a Python library grinds data beans into the incomplete by introducing missing values with different missing patterns.
Algorithms
PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series
MICE: Multivariate Imputation by Chained Equations
AutoImpute: a Python package for Imputation Methods
Impyute: a library of missing data imputation algorithms
❖ Must-Read Papers on Time-Series Imputation
The papers listed here may be not from top publications, some of them even are not deep-learning methods, but are all interesting papers related to time-series imputation that deserve reading to researchers and practitioners who are interested in this field.
Year 2023
[ICLR] Multivariate Time-series Imputation with Disentangled Temporal Representations [paper] [official code]
[ICDE] PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation [paper] [official code]
[ESWA] SAITS: Self-Attention-based Imputation for Time Series [paper] [official code]
[TMLR] Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models [paper] [official code]
[ICML] Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion [paper] [official code]
[ICML] Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation [paper] [official code]
[ICML] Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion [paper]
[ICML] Probabilistic Imputation for Time-series Classification with Missing Data [paper]
[KDD] Source-Free Domain Adaptation with Temporal Imputation for Time Series Data [paper] [official code]
[KDD] Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders [paper]
[KDD] An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series [paper]
[TKDE] Selective Imputation for Multivariate Time Series Datasets With Missing Values [paper] [official code]
[TKDE] PATNet- Propensity-Adjusted Temporal Network for Joint Imputation and Prediction using Binary EHRs with Observation Bias [paper]
[TKDD] Multiple Imputation Ensembles for Time Series (MIE-TS) [paper]
[CIKM] Density-Aware Temporal Attentive Step-wise Diffusion Model For Medical Time Series Imputation [paper]
Year 2022
[ICLR] Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks [paper] [official code]
[AAAI] Online Missing Value Imputation and Change Point Detection with the Gaussian Copula [paper] [official code]
[AAAI] Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation [paper]
[AAAI] Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values [paper]
Year 2021
[NeurIPS] CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [paper] [official code]
[AAAI] Generative Semi-supervised Learning for Multivariate Time Series Imputation [paper]
[VLDB] Missing Value Imputation on Multidimensional Time Series [paper]
[ICDM] STING: Self-attention based Time-series Imputation Networks using GAN [paper]
Year 2020
[AISTATS] GP-VAE: Deep Probabilistic Time Series Imputation [paper] [official code]
[CVPR] Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation [paper]
[ICLR] Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks [paper]
[TNNLS] Adversarial Recurrent Time Series Imputation [paper]
Year 2019
[NeurIPS] NAOMI: Non-Autoregressive Multiresolution Sequence Imputation [paper] [official code]
[IJCAI] E²GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation [paper] [official code]
[WWW] How Do Your Neighbors Disclose Your Information: Social-Aware Time Series Imputation [paper] [official code]
Year 2018
[NeurIPS] BRITS: Bidirectional Recurrent Imputation for Time Series [paper] [official code]
[Scientific Reports] Recurrent Neural Networks for Multivariate Time Series with Missing Values [paper] [official code]
[NeurIPS] Multivariate Time Series Imputation with Generative Adversarial Networks [paper] [official code]
Year 2017
[IEEE Transactions on Biomedical Engineering] Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks [paper] [official code]
Year 2016
[IJCAI] ST-MVL: Filling Missing Values in Geo-sensory Time Series Data [paper] [official code]
❖ Other Resources
Repos about General Time Series
LLMs and Foundation Models for Time Series and Spatio-Temporal Data
AI for Time Series (AI4TS) Papers, Tutorials, and Surveys
🏠 Visits
WenjieDu