CGCNImp: a causal graph convolutional network for multivariate time series imputation

BACKGROUND: Multivariate time series data generally contains missing values, which can be an obstacle to subsequent analysis and may compromise downstream applications. One challenge in this endeavor is the presence of the missing values brought about by sensor failure and transmission packet loss....

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Detalles Bibliográficos
Autores principales: Liu, Caizheng, Cui, Guangfan, Liu, Shenghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Algorithms and Analysis of Algorithms
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138184/
https://www.ncbi.nlm.nih.gov/pubmed/35634128
http://dx.doi.org/10.7717/peerj-cs.966

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138184/
https://www.ncbi.nlm.nih.gov/pubmed/35634128
http://dx.doi.org/10.7717/peerj-cs.966

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