STAN Based PM2.5 Prediction Model

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.