Civil aviation passenger volume prediction based on the fusion of modal decomposition and hybrid models
Aviation industry plays an important role in the transportation sector.Timely and accurate prediction of civil aviation passenger volume has gained keen academic interest.To address the low accuracy and missing high-dimensional features,we propose a prediction model combining complementary ensemble empirical modal decomposition(CEEMD)and support vector machine(SVM)in combination with the characteristics of Chinese civil aviation passenger volume.First,the data are decomposed by CEEMD to effectively tackle the complex features and trends.Second,the parameters of the SVM model are optimized by Particle Swarm Optimization Algorithm(PSOA)to ensure the model better adapts to the data features and provides accurate prediction results.Then,a combined CEEMD-PSOA-SVM prediction model is built to analyze the complex passenger volume data and improve the prediction results.The passenger volume data from 2005 to 2024 are selected for modeling and compared with the predictions of CEEMD-SVM,EMD-SVM,EEMD-SVM,EMD-PSO-SVM,EEMD-PSOA-SVM models by employing appropriate performance evaluation indexes.Our results show our CEEMD-PSOA-SVM model generates more accurate predictions of China's civil aviation passenger volumes.
complementary set empirical mode decompositionPSOASVMcombination predictionpassenger volume prediction
国家科技期刊平台
NSTL
万方数据
