Research on stock prediction based on improved VMD and RBF
In order to solve the problem of stock price prediction,the nonlinear analysis of the stock market is carried out by using chaos theory,and the variational mode decomposition with improved mutual information is combined with the neural network to propose the MVMD-RBF price prediction model.The daily closing price of the Shanghai Composite Index and CSI 300 were selected as the research objects for LASSO variable screening,phase space reconstruction,and finally mixed model prediction,and four models were selected for comparative analysis,and the results showed that the MVMD-RBF prediction effect was better than that of other models.This proves that the MVMD-RBF model has a good effect on predicting chaotic stock data.
stock priceMVMD-RBFLASSOphase space reconstructionchaotic time series