Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates

arXiv:2404.05324v1 Announce Type: new
Abstract: Due to the latest environmental concerns in keeping at bay contaminants emissions in urban areas, air pollution forecasting has been rising the forefront of all researchers around the world. When predicting pollutant concentrations, it is common to include the effects of environmental factors that influence these concentrations within an extended period, like traffic, meteorological conditions and geographical information. Most of the existing approaches exploit this information as past covariates, i.e., past exogenous variables that affected …

arxiv cs.ai cs.lg eess.sp forecasting future gnn type via