A Prediction Model for Soybean Meal Futures Price Based on CNN-ILSTM-SA

Predicting the price of soybean meal futures has always been a hot topic in the financial market. It is influenced by many factors and has complex nonlinear characteristics, which makes it difficult to predict accurately. This paper proposes a prediction model for soybean meal futures price based on CNN-ILSTM-SA, which consists of CNN (Convolutional Neural Network), ILSTM (Improved Long Short-Term Memory), and SA (Self-Attention mechanism). CNN can well perform feature extraction on the input time series data. The ILSTM model presented in this paper removes the output gate of LSTM (Long Short-Term Memory) and improves the forget gate and input gate, thus reducing ILSTM’s complexity, shortening training time, and improving learning ability. SA can get the correlation between different input terms and predicted values, and assign different weight coefficients according to the correlation level to improve prediction accuracy. To verify the CNN-ILSTM-SA’s effectiveness, we compare it with eighteen baseline models, and the experimental results show that it is optimal. The MAE (Mean Absolute Error) of CNN-ILSTM-SA is 39.0441, the smallest of all models. As the data of the soybean meal futures price fluctuates in the range of 3500-4500 in experiments, the proportion of the MAE value of 39.0441 is 0.87%-1.15%, which is at an extremely low level;
$\mathrm {R}^{2}$
(R-square) is 0.97487, which is closest to 1; The training time is 157.64s.