%0 Journal Article %A NIE Ling %A ZHANG Jian %A HU Maozheng %T Short-Term Traffic Flow Combination Prediction Based on CEEMDAN Decomposition %D 2022 %R 10.3778/j.issn.1002-8331.2106-0305 %J Computer Engineering and Applications %P 279-286 %V 58 %N 11 %X Short-term traffic flow prediction is an important guarantee for traffic flow guidance and control. In view of the randomness and complexity of traffic flow, a combined short-term traffic flow prediction model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) is proposed. Firstly, the CEEMDAN algorithm is used to decompose the nonlinear sequence adaptatively, and the traffic flow time series is decomposed into multiple time series components with different frequencies and complexity by CEEMDAN algorithm. Secondly, PE algorithm is used to analyze the random characteristics of each component. According to the different random characteristics of time series components, they are divided into high frequency, intermediate and low frequency sequence components. According to the random characteristics, GWO-BP model, GWO-LSSVM model and ARIMA model are established respectively. Finally, the prediction results of high frequency, intermediate and low frequency components are superimposed to obtain the final predicted value of short-term traffic flow. The simulation results show that compared with other prediction models, the combined prediction model based on CEEMDAN decomposition improves the prediction accuracy. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2106-0305