基于数据分解的AQI的CEEMD-Elman神经网络预测研究

针对Elman神经网络在预测空气质量指数（AQI）时易受到数据非平稳性的影响导致预测趋势良好但准确度较低的问题，提出以互补集合经验模态分解（Complementary Ensemble Empirical Mode Decomposition，CEEMD）为基础的CEEMD-Elman模型.应用CEEMD对AQI序列分解成不同时间尺度上的本征模态函数分量和剩余分量，进而首次将对非平稳的AQI序列的预测研究转化为对多个平稳的本征模态函数分量的研究.分别与Elman单一模型、EMD-Elman模型、BP单一模型及CEEMD-BP模型进行实验对比.结果表明：应用该方法建立的模型的均方误差、平均绝对误差和平均绝对百分比误差分别为4.80、0.71、1.84%，均小于其他模型结果；对应空气质量等级预报正确天数的频率为94.12%.该模型能有效的降低非平稳性对实验预测结果的影响，实现对空气质量等级的准确预报；该研究为进一步预测AQI的走向提供了有效依据，也为政府决策和管理部门制定空气污染控制提供了更充分的参考.

AQI prediction of CEEMD-Elman neural network based on data decomposition

Elman neural network (ENN) is susceptible to the non-stationary of data when it is used to predict the Air Quality Index (AQI), resulting in a good forecasting trend but low accuracy. Based on complementary ensemble empirical modal decomposition (CEEMD), a new hybrid model related to ENN was proposed in this paper. Firstly, CEEMD was employed to decompose the AQI sequence into a finite number of intrinsic mode functions (IMFs) at different time scales and one residue. Secondly, partial autocorrelation function was used to calculate the lag periods of the input variables of each IMF in ENN. Finally, the predicted values of each IMF were summed up to obtain the final predicted result. The study of the AQI without stationarity sequence was then transformed into the study of steady IMFs. The experimental results show that the mean square error, the mean absolute error, and the mean absolute percent error were respectively 4.80, 0.71, and 1.84% which were all less than those of the single Elman network, EMD-Elman model, BP network and CEEMD-BP model. Furthermore, the frequency of the correct forecast for the corresponding air quality grade was 94.12%. It has been concluded that the new model could reduce the volatility impact of real AQI data and effectively predict the air quality grade. This study not only provides an effective evidence to further predict the trend of AQI, but provides a better reference for government decision-making and pollution control formulation of management departments.