Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic and social growth. Forecasting air quality has become an increasingly important endeavor with significant social impacts, especially in emerging countries like China. In this paper, we present a novel Transformer architecture termed AirFormer to collectively predict nationwide air quality in China, with an unprecedented fine spatial granularity covering thousands of locations. AirFormer decouples the learning process into two stages -- 1) a bottom-up deterministic stage that contains two new types of self-attention mechanisms to efficiently learn spatio-temporal representations; 2) a top-down stochastic stage with latent variables to capture the intrinsic uncertainty of air quality data. We evaluate AirFormer with 4-year data from 1,085 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. Our source code is available at https://github.com/yoshall/airformer.

本文介绍了一种新的Transformer架构AirFormer，用于预测中国全国范围内空气质量，具有前所未有的千亿级细粒度，包括两个新的自我注意机制来高效学习时空表示，并使用潜在变量来捕捉空气质量数据的固有不确定性。我们使用中国大陆1,085个气象站的四年数据来评估AirFormer，并且相比于最先进模型，AirFormer在72小时未来预测上将预测误差降低了5％〜8％。

AirFormer: 利用 Transformers 预测中国全国空气质量