STAN Based PM2.5 Prediction Model

被引：0

作者：

Xu, Zhe ^{[1
]}

Huo, Qingzhou ^{[1
]}

Lv, Yi ^{[1
]}

机构：

[1] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China

来源：

2019 CHINESE AUTOMATION CONGRESS (CAC2019) | 2019年

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

air quality; spatiotemporal correlation; LSTM; attention mechanism; time series prediction;

D O I：

10.1109/cac48633.2019.8996613

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

PM2.5 elements have a great impact on air quality, so it is of great significance to predict PM2.5 concentration for People's Daily life and health. Aiming at the problem of low prediction accuracy of existing models, we propose a spatial-temporal attention neural network (STAN). Firstly, we introduce a spatial attention module to adaptively extract spatial features between monitoring stations. Then, we use a temporal attention to extract features from encoder hidden states across time series. We evaluate the STAN on PM2.5 prediction with data from Beijing observation stations, and the results show that it is superior to ARIMA LSTM and Seq2seq models in predicting PM2.5 concentration.

引用

页码：3482 / 3487

页数：6

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