Soybean Futures Price Forecasting Based on CEEMDAN-SE-CNN-BiLSTM Model
In order to improve the accuracy of soybean futures price prediction,a multi-step futures price prediction model based on"decomposition-reorganization-prediction-integration"is improved by utilizing internal and external information of soybean futures market.The soybean price series are subjected to adaptive noise-complete ensemble empirical modal decomposition(CEEMDAN)to obtain the IMF components and error terms.The IMF component with small correlation coefficients with the original series is screened and eliminated,and then the decomposed series are reorganized by the sample entropy(SE)algorithm.Using the optimized CNN-BiLSTM prediction model to predict the reconstructed sequence,the final prediction value is obtained after integration.The empirical results show that the proposed CEEMDAN-SE-CNN-BiLSTM model generally outperforms the benchmark agricultural futures prediction models such as LSTM and CNN-LSTM in predicting soybean futures prices.
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