Autoregressive integrated moving average (ARIMA) model, back propagation (BP) neutral network and long short-term memory (LSTM) are used to predict the daily concentration of PM_2.5 in 2019 in Guangzhou city of China from 2015 to 2019. The effect of ensemble empirical mode decomposition (EEMD), temporal resolution on model prediction is explored in this paper. The results show that EEMD is able to improve significantly the prediction ability of the model on the low-frequency part of PM_2.5 sequence. Increased temporal resolution can improve the prediction accuracy, with more input data. Since PM_2.5 (t-1) is used as the input data, the model can only predict PM_2.5 for 1 day in advance. To increase the prediction time window, we adopt a rolling forecast method, using PM_2.5 (t) prediction value as the input data for PM_2.5 (t+1). The result shows that the rolling forecast method allows the model to forecast PM_2.5 (t+n) with a comparable MAE compared to the experiment without the rolling forecast method. In this paper, the ARIMA model (the time accuracy of input data is 6 hours) has the best prediction accuracy, and the minimum MAE value can reach 6.478. 

Guangzhou city, PM_2.5, autoregressive integrated moving average model (ARIMA), back propagation (BP), long short-term memory networks (LSTM), ensemble empirical mode decomposition (EEMD)