基于非结构数据和EMD-WTS二层分解的AQI组合预测方法

AQI Combined Forecast Method Based on Unstructured Data and EMD-WTS Two-layer Decomposition

针对具有高复杂性与非平稳性的空气质量指数(AQI)时间序列,提出一种融合非结构数据的EMD-WTS二层分解组合预测模型;首先,筛选百度指数关键词并提取对应数据,运用局部线性嵌入算法(LLE)对之降维;其次,对AQI历史序列与降维结果进行经验模态分解(EMD)与重构;接着,对所得高频项进行小波分解(WT)与重构;然后,运用Holt指数平滑法、支持向量回归(SVR)与人工神经网络(ANN)分别对二层分解结果与原始低频、趋势项进行组合预测并运用BP神经网络集成;最后,叠加集成结果得到AQI预测值;对比实验说明预测方法充分利用了多源数据信息,具有较高的预测精度。

To deal with the highly random and unstable sequence of Air Quality Index(AQI),an EMD-WTS two-layer decomposition and unstructured data based combined forecast model is proposed.Firstly,the Baidu index keywords are filtered and the corresponding data is extracted,after which the locally linear embedding(LLE)is applied to reduce the dimensions.Secondly,the empirical modal decomposition(EMD)and reconstruction are carried out on AQI historical sequence and dimension-lowering results.Then,the wavelet transform(WT)is adopted to decompose and reconstruct the gained high-frequency sequence.After reconstruction,the Holt exponential smoothing,support vector regression(SVR)and artificial neural network(ANN)are used to forecast the results of two-layer decomposition and the original low frequency and trend sequence.Subsequently,the forecast results are integrated by BP neural network.Eventually,the gained forecast results above are added up and the final prediction results of AQI are obtained.The comparative experiment’s results demonstrate that the forecast model aforesaid can make full use of a variety of data information,and the prediction accuracy is quite high.