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) 

利用差分整合移动平均自回归模型(ARIMA)、后向传播神经网络(BP)以及长短期记忆神经网络(LSTM), 对广州市2015—2019年的PM_2.5浓度数据进行训练和预报, 研究集合经验模态(EEMD)分解和时间分辨率对不同模型预报准确性的影响。结果表明, EEMD分解可以显著地提升低频分量的预报效果; 提高输入数据的时间分辨率可以提升预报效果, 尤其在ARIMA自回归模型预报中较为明显, 用神经网络进行预报时需要考虑输入数据量增加带来模型复杂度增加的问题。由于模型使用前一天(t -1)的PM_2.5作为输入数据, 即只能预报t+1天的PM_2.5值。为增加模型的预报时效, 采用滚动预报的方式对模型进行优化, 能够显著地提升预报时效, 实现对t+n天的连续预报, 且预报误差与后报结果相当。将时间精度为6 h的数据作为输入, 用ARIMA模型进行预报的效果最好, 最小MAE值为6.478。