基于分解集成方法的空中交通流量短期预测

为对空中交通流量进行短期预测，提出了基于分解集成方法的组合预测模型。首先，应用EEMD方法将流量时序数据分解为若干个分量；其次，应用排列熵计算各分量的复杂度，复杂度高于0.5的归为高频分量，其余归为低频分量；然后，高频分量采用BP神经网络算法进行预测，低频分量采用最小二乘法进行预测；接着，对分量的预测结果进行加和集成，得到了最终的预测值。最后，采集实际运行数据进行算例分析。通过比较1~6 h和7~12 h的预测结果，本文模型在1~6 h的EC值为0.905，准确度更高。与EMD-BP-OLS模型、BP模型进行比较，本文模型的评价指标均优于其他模型。通过比较60 min，30 min，15 min时间尺度数据的预测结果，60 min时间尺度的EC值为0.924，准确度最高。结果表明，本文提出的模型是可行的和有效的，更适用于短期流量预测。

Short Term Forecasting of Air Traffic Flow Based on Decomposition and Integration Method

In order to forecast air traffic flow in short term, a combined forecasting model based on decomposition and integration method is proposed. Firstly, the EEMD method is used to decompose the traffic time series data into several components; secondly, the permutation entropy is used to calculate the complexity of each component. The high-frequency component is classified as high-frequency component and the rest is classified as low-frequency component; then, BP neural network algorithm is used to predict the high-frequency component, and the least square method is used to predict the low-frequency component; then, the prediction results of the components are added Finally, the final prediction value is obtained. Finally, the actual operation data are collected for example analysis. By comparing the prediction results of 0 ~ 6 h and 6 ~ 12 h, the EC Value of the model in 0 ~ 6 h is 0.905, and the accuracy is higher. Compared with EMD-BP-OLS model and BP model, the evaluation index of this model is better than other models. By comparing the prediction results of 60 min, 30 min and 15 min time scale data, the EC Value of 60 min time scale is 0.924, with the highest accuracy. The results show that the model proposed in this paper is feasible and effective, and more suitable for short-term flow forecasting.