时间序列-插补-顶会顶刊论文汇总【论文/源码链接】

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This repository provides access to the TSI-Bench: Benchmarking Time Series Imputation dataset, a valuable resource for researchers.

PyPOTS Research](https://pypots.com).
Files and parameters necessary for replicating the experimental outcomes are accessible within the benchmark_code folder.
This section provides a curated list of essential papers on time-series imputation, alongside an extensive collection of relevant toolkit resources.

🤗 Contributions to update new resources and articles are very welcome!

The papers listed here do not originate from highly regarded journals, and among them, some are not deep learning-based techniques. However, they represent a collection of intriguing time-series imputation studies that merit attention for researchers and practitioners in this domain.

Year 2024

[KDD] ImputeFormer: 基于低秩性的Transformer模型用于普适时空插补
[paper]
[official code]

BayOTIDE: A Bayes-based Sequential Approach for Multivariate Time Series Data Imputation with Functional Decomposition Approach

[ICLR] Conditioned Information Compression-Based Method for Time Series Imputation [paper] [official code]

[AISTATS] SADI: Data-Aware Diffusion Model-Based Imputation for Handling Incomplete Temporal Electronic Health Records
[paper]
[official code]

Year 2023

[ICLR] Multivariate Time-series Imputation with Disentangled Temporal Representations
[paper]
[official code]

[ICDE] PriSTI: A Conditioned Diffusion System for Space-time Data Imputation
[paper]
[official code]

[ESWA] SAITS: Self-Attention-based Imputation for Time Series
[paper]
[official code]

[TMLR] Diffusion-based techniques for Time Series Data Completion and Prediction Using 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 in Applications in Probabilistic Time Series Imputation, with associated paper paper and official code 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] 基于观测值一致扩散模型用于多变量时间序列填补缺失值 [paper]

[TKDE] Selective Filling for Effective Techniques in Multivariate Time Series Datasets Containing Missing Observations

[TKDE] PATNet- Propensities-Adjusted Temporal Network aimed at joint imputation and prediction based on binary EHRs considering 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 the Need for Imputation: Using a Decision Tree Framework for Achieving Fair Predictions with Missing Values
[paper]

Year 2021

[NeurIPS] CSDI: Conditioned Score-based Diffusion Model for Probabilistic Time Series Estimation
[paper]
[official code]

[AAI] Generative-based Semi-supervised Learning for Multivariate Time Series Imputation [paper]

[VLDB] Missing Value Imputation on Multidimensional Time Series
[paper]

[ICDM] 基于自注意力机制的时间序列插补网络：STING方法及其在GAN框架下的应用 [paper]

Year 2020

[AISTATS] a-GP-VAE: A Probabilistically Deep Approach to Time Series Imputation
[paper]
[official code]

一种基于模仿机制的非自回归模型用于运动轨迹预测与缺失插补

[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-sequential autoregession-free multiscale sequence data imputation]: 由该会议接收并展示的一种无自回归多分辨率序列数据填充方法。
[paper]
[official code]

[IJCAI] E²GAN: End-to-End based Generative Adversarial Network for the Imputation of Multivariate Time Series
[paper]
[official code]

[WWW] How Do Your Neighbors Disclose Your Information: Social-Aware Time Series Imputation
[paper]
[official code]

Year 2018

[NeurIPS] Bidirectional Recurrent Interpolation Transformer (BRITS): Bidirectional Recurrent Interpolation for Time Series
[paper]
[official repository]

[Scientific Reports] Recurrence-based Neural Networks in the Analysis of Multivariable Time Series with Missing Data Handling
[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

Articles about General Missingness and Imputation

[blog] Data Imputation: An essential yet overlooked problem in machine learning

[Journal of Big Data] A survey on missing data in machine learning
[paper]

Repos about General Time Series

Transformers in Time Series

Large language models and base-level language models: exploring their application to temporal data sets that incorporate both temporal and spatial dimensions.

AI for Time Series (AI4TS) Publications, Guides, and Overviews

❖ Citing This Work

Should you discover this repository and the PyPOTS Ecosystem beneficial for your endeavors, please kindly follow the stars of this project and properly cite our benchmark paper, survey paper, and PyPOTS in accordance with the guidelines provided.

    @article{du2024tsibench,
    title={TSI-Bench: Benchmarking Time Series Imputation},
    author={Wenjie Du and Jun Wang and Linglong Qian and Yiyuan Yang and Fanxing Liu and Zepu Wang and Zina Ibrahim and Haoxin Liu and Zhiyuan Zhao and Yingjie Zhou and Wenjia Wang and Kaize Ding and Yuxuan Liang and B. Aditya Prakash and Qingsong Wen},
    journal={arXiv preprint arXiv:2406.12747},
    year={2024}
    }

    @article{wang2024deep,
    title={Deep Learning for Multivariate Time Series Imputation: A Survey},
    author={Jun Wang and Wenjie Du and Wei Cao and Keli Zhang and Wenjia Wang and Yuxuan Liang and Qingsong Wen},
    journal={arXiv preprint arXiv:2402.04059},
    year={2024}
    }

    @article{du2023pypots,
    title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
    author={Wenjie Du},
    journal={arXiv preprint arXiv:2305.18811},
    year={2023},
    }

🏠 Visits [

](https://github.com/WenjieDu/Awesome_Imputation)