Imputation of Missing Values in Time Series Using an Adaptive-Learned Median-Filled Deep Autoencoder-Reference-Cited by-同舟云学术

Imputation of Missing Values in Time Series Using an Adaptive-Learned Median-Filled Deep Autoencoder

Published:2023-02 Issue:2 Volume:53 Page:695-706
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Pan Zhuofu¹^ORCID,Wang Yalin¹^ORCID,Wang Kai¹^ORCID,Chen Hongtian²^ORCID,Yang Chunhua¹^ORCID,Gui Weihua¹^ORCID

Affiliation:

1. School of Automation, Central South University, Changsha, China

2. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Science and Technology Innovation Program of Hunan Province in China

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/10016773/09768200.pdf?arnumber=9768200

Reference34 articles.

1. Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems

2. GMM and optimal principal components-based Bayesian method for multimode fault diagnosis

3. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

4. Fault detection and diagnosis in industrial systems;chiang;Advanced Textbooks in Control and Signal Processing,2000

5. A classification-driven neuron-grouped SAE for feature representation and its application to fault classification in chemical processes

Cited by 38 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel data-characteristic-driven modeling approach for imputing missing value in industrial statistics: A case study of China electricity statistics;Applied Energy;2024-11

2. A new attention-based CNN_GRU model for spatial–temporal PM2.5 prediction;Environmental Science and Pollution Research;2024-08-23

3. Incomplete data evidential classification with inconsistent distribution;Information Sciences;2024-08

4. Mixed-Type Imputation for Missing Data Credal Classification via Quality Matrices;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2024-08

5. Continuous imputation of missing values in time series via Wasserstein generative adversarial imputation networks and variational auto-encoders model;Physica A: Statistical Mechanics and its Applications;2024-08