# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Case Studies
#### 4.2.4 Comparative Analysis of Results  
Due to the strong nonlinear mapping ability of the long short-term memory neural network and the strong randomness and volatility of the four high-frequency component sequences of IMF\({}_{1}\), IMF\({}_{2}\), IMF\({}_{3}\), and IMF\({}_{4}\), the BGWO-LSTM model was selected for prediction in this study. The initial population number was set to 30, the maximum number of iterations was 100, the lb value was 3-20, the step size was 1, the ls value was 5-50, and the step size was 5. The ep value was 50 to 300, the step size was 10, the dp value was 0.3 to 0.61, and the step size was 0.01. The high-frequency component prediction is shown in Figure 9.  
In Figure 9, it can be seen that IMF\({}_{1}\) had the highest frequency, the strongest volatility, the greatest difficulty in forecasting, and the largest error in prediction. As the component sequence grew, the degree of fluctuation of the data decreased, the error of prediction was gradually reduced, and IMF\({}_{4}\) achieved a good fitting effect. Due to the good nonlinear mapping ability of support vector machine, the three intermediate-frequency component sequences, IMF\({}_{5}\), IMF\({}_{6}\), and IMF\({}_{7}\) were characterized by certain randomness and volatility. The IGWO-SVM model was selected for prediction. The initial population number was set to 30, and the maximum number of iterations was set 100. The intermediate-frequency component prediction is shown in Figure 10.  
In Figure 10, it can be seen that the intermediate-frequency components had a certain degree of volatility, and the prediction difficulty was less than that of the high-frequency component sequence. Among the three intermediate-frequency components, the IMF\({}_{5}\) prediction was the most difficult, and the error of the prediction was relatively large. With  
Due to the convenient modeling of the KNN, the number of calculations was low, the model training time was greatly reduced compared to BGWO-LSTM and IGWO-SVM, and it also had good prediction performance for relatively stable data, so the KNN model was selected to predict a relatively stable component sequence IMF\({}_{8}\). The low-frequency component prediction is shown in Figure 11. As can be seen in Figure 11, the KNN model had a good predictive result for IMF\({}_{8}\). It can be seen in Figures 9-11 that different models were selected to predict different component sequences, and the prediction results are quite ideal. Then, the prediction results of each component sequence were superimposed to obtain the output results of the final combined model and the output results were comprehensively compared with the single models (BGWO-LSTM, IGWO-SVM, and KNN) to evaluate the advantages and disadvantages of the combined model and the single prediction model proposed in this paper. The final results of each traffic flow prediction model are shown in Figure 12, where the horizontal axis represents the time series points and the vertical axis represents the traffic flow through this section of the road in 5 min. Figure 9: Prediction results of the high-frequency component sequences. (**a**) IMF1 component prediction results. (**b**) IMF2 component prediction results. (**c**) IMF3 component prediction results. (**d**) IMF4 component prediction results. Figure 10: Prediction results of the intermediate-frequency component sequence. (**a**) IMF5 component prediction results. (**b**) IMF6 component prediction results. (**c**) IMF7 component prediction results. In Figure 12, it can be seen that these four prediction models could predict the overall trend of traffic flow in a day for the time periods when the traffic flow was relatively stable and the changes were not large, such as in the timeline of [0, 50]. The prediction results of the four prediction models are relatively close to the actual value, but the prediction results are very different in the time periods of some traffic flow mutations.