论文标题
机构：通过变压器预测中国的全国性空气质量
AirFormer: Predicting Nationwide Air Quality in China with Transformers
论文作者
论文摘要
空气污染是影响人类健康和生计的关键问题，也是经济和社会增长的障碍之一。预测空气质量已成为越来越重要的社会影响，尤其是在中国等新兴国家。在本文中，我们介绍了一种新颖的变压器建筑，称为Airformer，以共同预测中国的全国性空气质量，其前所未有的精细空间粒度覆盖了数千个位置。机构将学习过程分为两个阶段 - 1）一个自下而上的确定性阶段，其中包含两种新型的自我发项机制，以有效地学习时空表示； 2）具有潜在变量的自上而下的随机阶段，可捕获空气质量数据的内在不确定性。我们通过来自中国大陆的1,085个站点的4年数据来评估AirFormer。与最先进的模型相比，在72小时的未来预测中，气壁将预测错误降低了5％〜8％。我们的源代码可在https://github.com/yoshall/airformer上找到。
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.
