Continuous imputation of missing values in time series via Wasserstein generative adversarial imputation networks and variational auto-encoders model-Reference-Cited by-同舟云学术

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Continuous imputation of missing values in time series via Wasserstein generative adversarial imputation networks and variational auto-encoders model

Published:2024-08 Issue: Volume:647 Page:129914
ISSN:0378-4371
Container-title:Physica A: Statistical Mechanics and its Applications
language:en
Short-container-title:Physica A: Statistical Mechanics and its Applications

Author:

Wang Yunsheng^ORCID,Xu Xinghan^ORCID,Hu Lei,Liu Jianwei,Yan Xiaohui,Ren Weijie^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference48 articles.

1. A generative model for vehicular travel time distribution prediction considering spatial and temporal correlations;Shao;Phys. A,2023

2. A two-stage causality method for time series prediction based on feature selection and momentary conditional independence;Ma;Phys. A,2022

3. Adaptive graph convolutional recurrent network for traffic forecasting;BAI,2020

4. Missing traffic data imputation considering approximate intervals: A hybrid structure integrating adaptive network-based inference and fuzzy rough set;Tang;Phys. A,2021

5. Imputation of missing data with neural networks for classification;Choudhury;Knowl.-Based Syst.,2019

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