Abstract
The prediction research of the stock market prices is of great significance. Based on the secondary decomposition techniques of variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD), this paper constructs a new hybrid prediction model by combining with extreme learning machine (ELM) optimized by the differential evolution (DE) algorithm. The hybrid model applies VMD technology to the original stock index price sequence to obtain different modal components and the residual item, then applies EEMD technology to the residual item, and then superimposes the prediction results of the DE-ELM model for each modal component and the residual item to obtain the final prediction results. In order to verify the validity of the model, this paper constructs a series of benchmark models and, respectively, tests the samples of the S&P 500 index and the HS300 index by one-step, three-step, and five-step forward forecasting. The empirical results show that the hybrid model proposed in this paper achieves the best prediction performance in all prediction scenarios, which indicates that the modeling idea focusing on the residual term effectively improves the prediction performance of the model. In addition, the prediction effect of the model combined with the decomposition technology is superior to the single DE-ELM model, where the secondary decomposition technique has a significant decomposition advantage compared to the single decomposition technique.
Highlights
Introduction ofMethodology is paper builds a combination model (VMD-RES.-EEMDDE-extreme learning machine (ELM)) based on the secondary decomposition technique and the machine learning method to predict the price of grain futures
Based on the advantages of variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD) technology, and extreme learning machine, this paper proposes a combined model VMD-RES.-EEMD-DEELM, which combines secondary decomposition technology and the artificial intelligence algorithm
This article takes the S&P500 index and the HS300 index as experimental samples and eRMSE, eMAE, and eMAPE as evaluation indexes to test the performance of the VMD-RES.-EEMDDE-ELM model compared with other seven benchmark models in the results of one-step, three-step, and five- step forward prediction. e conclusions of empirical analysis are as follows: (1) e VMD-RES.-EEMD-differential evolution (DE)-ELM hybrid model proposed in this paper takes full advantage of the secondary decomposition, and solves the problem that the residual term is not fully considered in the traditional time series prediction model based on VMD technology
Summary
Scholars make in-depth research on the prediction of univariate prediction. ere are three main types of prediction models: traditional econometric models, artificial intelligence models, and hybrid models. E method firstly uses the decomposition algorithm to process the original sequence with a large amount of noise and models each decomposed component with the prediction model. Wei [42] proposed a mixed time series model based on empirical mode decomposition to predict the stock prices of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Stock Index (HSI). VMD can effectively separate components with similar frequencies and improve the accuracy of original sequence decomposition It has been widely combined with various prediction models by scholars in empirical research and applied in the prediction research of the energy price market or the financial market [36, 48]. 2. Introduction of Methodology is paper builds a combination model (VMD-RES.-EEMDDE-ELM) based on the secondary decomposition technique and the machine learning method to predict the price of grain futures.
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