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Air pollution is a crucial issue affecting human health and livelihoods, aswell as one of the barriers to economic and social growth. Forecasting airquality has become an increasingly important endeavor with significant socialimpacts, especially in emerging countries like China. In this paper, we presenta novel Transformer architecture termed AirFormer to collectively predictnationwide air quality in China, with an unprecedented fine spatial granularitycovering thousands of locations. AirFormer decouples the learning process intotwo stages -- 1) a bottom-up deterministic stage that contains two new types ofself-attention mechanisms to efficiently learn spatio-temporal representations;2) a top-down stochastic stage with latent variables to capture the intrinsicuncertainty of air quality data. We evaluate AirFormer with 4-year data from1,085 stations in the Chinese Mainland. Compared to the state-of-the-art model,AirFormer reduces prediction errors by 5%~8% on 72-hour future predictions. Oursource code is available at https://github.com/yoshall/airformer.

AirFormer: Predicting Nationwide Air Quality in China with Transformers