The time series of railway freight volume is affected by many factors,and the data has volatility and randomness,which leads tothe low prediction?accuracy. In order to improve the prediction accuracy of railway freight volume,a railway freight volume predictionmethod based on decomposition?
and integration framework is proposed. Firstly,relevant influencing factors were screened,and PrincipalComponent Analysis ( PCA) was used for?
dimension reduction to obtain the principal components. Then the Complementary EnsembleEmpirical Mode Decomposition ( CEEMD) was used t
o decompose the historical data of railway freight volume into a relatively stablecomponent. Sample Entropy ( SE) was used to evaluate component?
complexity and reassemble components,which were combined withprincipal components to form a new data set. Finally,the new data set was predicted?
in the Extreme Learning Machine ( ELM) modeloptimized by Bald Eagle Search ( BES) algorithm to obtain the prediction result of the reconstituted component,and the final predictionresult was obtained by superimposing the prediction component. Compared with other algorithms,the proposed?
CEEMD-BES-ELM decomposition integration method has advantages in railway freight volume prediction and can effectively improve the accuracy?
of railwayfreight volume prediction.