A Novel Hybrid Model of CEEMDAN-CNN-SAGU for Shanghai Copper Price Prediction

As an essential metal, copper has the advantages of electrical conductivity and ductility, which is widely used in power transmission, electronics manufacturing and machining. The fluctuation of copper price has a great impact on the industry, especially on the development of the national economy, so predicting copper price has a great significance for economic development. However, traditional time series prediction models’ prediction accuracy is low. Therefore, this paper proposes a Shanghai copper price forecasting model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), Self Attention Gated Unit (SAGU), named CEEMDAN-CNN-SAGU. CEEMDAN decomposes and reconstructs the Shanghai copper price data. In this paper, the zero-cross rate of the Intrinsic Mode Function (IMF) components is calculated, the IMF component with large noise is removed, and the remaining IMFs and Residual term (Res) are reorganized to obtain the high-frequency and low-frequency components. CNN performs convolution operations on the reconstructed components to extract time series features. SAGU is a new time series data prediction model proposed in this paper. SAGU includes two gated units (a forgetting gate and an input gate), and two data processing modules (Self Attention (SA) and Transition (Tra)). The SA module is responsible for processing the input data, redistributing the weight coefficients of different data, and improving the model’s attention to important information. The Tra module improves the low limit of the forgetting gate’s output so that the gated unit can keep the cell state from the previous moment to the current moment as much as possible. This paper uses Shanghai copper trading data from Shanghai Futures Exchange as experimental data. The comparison experiment with the other eleven prediction models shows that the CEEMDAN-CNN-SAGU model outperforms other models in all evaluation indexes.