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Research on grain futures price forecasting based on secondary decomposition and ensemble learning
TANG Zhenpeng, WU Junchuan, ZHANG Tingting, DU Xiaoxu, CHEN Kaijie
Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (11) : 2837-2849.
PDF(1890 KB)
PDF(1890 KB)
Research on grain futures price forecasting based on secondary decomposition and ensemble learning
Based on the idea of secondary decomposition and ensemble learning, we build the VMD-EEMD-DE-ELM-DE-ELM model, select soybeans, wheat and rice futures listed on the CBOT exchange as representatives of international grain futures, and predict its future price trend. In view of the existing research that directly ignore the residual items after VMD decomposition, we introduce the idea of secondary decomposition to perform the EEMD decomposition and ensemble prediction of its residual items for the first time. This method can capture the rich information contained in the residual items, thereby helping to improve the model's prediction effect on the original sequence. At the same time, because of the shortcomings of the existing model which use equal weights to reconstruct the prediction results of components, we draw on the idea of ensemble learning and introduces the DE-ELM meta-learner to optimize the reconstruction weights to obtain the best overall prediction results of the model. The empirical results show that the model proposed by us has a significant predictive advantage over the existing models.
grain futures / prediction / secondary decomposition / ensemble learning {{custom_keyword}} /
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The General Program of National Natural Science Foundation of China (71573042, 71973028)
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