  Accurate and timely air quality and weather predictions are of great
importance to urban governance and human livelihood. Though many efforts have
been made for air quality or weather prediction, most of them simply employ one
another as feature input, which ignores the inner-connection between two
predictive tasks. On the one hand, the accurate prediction of one task can help
improve another task's performance. On the other hand, geospatially distributed
air quality and weather monitoring stations provide additional hints for
city-wide spatiotemporal dependency modeling. Inspired by the above two
insights, in this paper, we propose the Multi-adversarial spatiotemporal
recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather
predictions. Specifically, we first propose a heterogeneous recurrent graph
neural network to model the spatiotemporal autocorrelation among air quality
and weather monitoring stations. Then, we develop a multi-adversarial graph
learning framework to against observation noise propagation introduced by
spatiotemporal modeling. Moreover, we present an adaptive training strategy by
formulating multi-adversarial learning as a multi-task learning problem.
Finally, extensive experiments on two real-world datasets show that MasterGNN
achieves the best performance compared with seven baselines on both air quality
and weather prediction tasks.
