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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....
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
PeerJ Inc.
2022
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| Materias: | |
| 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 |