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

ABSTRACT:

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. Imputation is the usual remedy in such circumstances. However, in some multivariate time series data, the complex correlation and temporal dependencies, coupled with the non-stationarity of the data, make imputation difficult.

Mehods

To address this problem, we propose a novel model for multivariate time series imputation called CGCNImp that considers both correlation and temporal dependency modeling. The correlation dependency module leverages neural Granger causality and a GCN to

SUBMITTER: Liu C 

PROVIDER: S-EPMC9138184 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature