Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates
Antonio Giganti Sara Mandelli Paolo Bestagini Umberto Giuriato Alessandro D'Ausilio Marco Marcon Stefano Tubaro
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 the pollutant but were not affected by it. In this
paper, we present a novel forecasting methodology to predict NO$_2$
concentration via both past and future covariates. Future covariates are
represented by weather forecasts and future calendar events, which are already
known at prediction time. In particular, we deal with air quality observations
in a city-wide network of ground monitoring stations, modeling the data
structure and estimating the predictions with a Spatiotemporal Graph Neural
Network (STGNN). We propose a conditioning block that embeds past and future
covariates into the current observations. After extracting meaningful
spatiotemporal representations, these are fused together and projected into the
forecasting horizon to generate the final prediction. To the best of our
knowledge, it is the first time that future covariates are included in time
series predictions in a structured way. Remarkably, we find that conditioning
on future weather information has a greater impact than considering past
traffic conditions. We release our code implementation at
https://github.com/polimi-ispl/MAGCRN.