Deep Learning for Multivariate Time Series Imputation: A Survey

Jun Wang, Wenjie Du, Yiyuan Yang, Linglong Qian, Wei Cao +4 more
2/6/2024
cs.LGcs.AI

Abstract

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.

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Code Implementations(3)

WenjieDu/Awesome_ImputationOfficial100%
BSD-3-Clause

Awesome Deep Learning for Time-Series Imputation, including an unmissable paper and tool list about applying neural networks to impute incomplete time series containing NaN missing values/data

41945Shell, PythonDec 12, 20232 months agoBSD-3-Clause
benchmarkdata-miningdeep-learningimputationincomplete-time-series+12 more
WenjieDu/PyPOTSCommunity100%
PyTorchBSD-3-Clause

A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values

2,007184Shell, PythonMar 29, 20221 months agoBSD-3-Clause
anomaly-detectionclassificationclusteringdata-analysisdata-mining+10 more
WenjieDu/SAITSCommunity100%
PyTorchMIT

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

50570Shell, PythonDec 7, 20218 months agoMIT
attentionattention-mechanismdeep-learningimputationimputation-model+15 more

Cite this paper

@article{wang2024deep,
  title  = {Deep Learning for Multivariate Time Series Imputation: A Survey},
  author = {Jun Wang and Wenjie Du and Yiyuan Yang and Linglong Qian and Wei Cao and Keli Zhang and Wenjia Wang and Yuxuan Liang and Qingsong Wen},
  year   = {2024},
  eprint = {2402.04059},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.04059v3}
}

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