Research on agricultural commodity futures price prediction based on ensemble empirical mode decomposition
Deep learning performs excellent in predicting nonlinear time series,and it does not con-sider the endogeneity between variables.This paper integrates the Ensemble Empirical Mode De-composition(EEMD)method with Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and Gated Recurrent Unit(GRU),and a model for forecasting agricultural com-modity futures prices based on integrated decomposition is constructed.Taking Chinese corn,cot-ton and soybean futures prices as examples,the original futures price signal is decomposed by EE-MD,and then the decomposed vectors are input into the deep learning models for training.Finally,it is concluded that EEMD-GRU model is the optimal price prediction model.The results demon-strate that compared with the individual deep learning models,the proposed integrated EEMD mod-el has obvious advantages in predictive accuracy and stronger generalization ability.
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