Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations

arXiv:2402.12598v1 Announce Type: new
Abstract: Virtual sensing techniques allow for inferring signals at new unmonitored locations by exploiting spatio-temporal measurements coming from physical sensors at different locations. However, as the sensor coverage becomes sparse due to costs or other constraints, physical proximity cannot be used to support interpolation. In this paper, we overcome this challenge by leveraging dependencies between the target variable and a set of correlated variables (covariates) that can frequently be associated with each location of interest. From …

abstract arxiv constraints costs coverage cs.ai cs.lg graph graph-based locations multivariate paper sensing sensor sensors support temporal type virtual