CASPER: Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation

arXiv:2403.11960v1 Announce Type: new
Abstract: Spatiotemporal time series is the foundation of understanding human activities and their impacts, which is usually collected via monitoring sensors placed at different locations. The collected data usually contains missing values due to various failures, which have significant impact on data analysis. To impute the missing values, a lot of methods have been introduced. When recovering a specific data point, most existing methods tend to take into consideration all the information relevant to that point …

abstract analysis arxiv causality cs.lg data data analysis foundation graph graph neural networks human impact impacts imputation locations missing values monitoring networks neural networks sensors series stat.ml time series type understanding values via