A Novel Hybrid Model of CNN-SA-NGU for Silver Closing Price Prediction

Abstract

Silver is an important industrial raw material, and the price of silver has always been a concern of the financial industry. Silver price data belong to time series data and have high volatility, irregularity, nonlinearity, and long-term correlation. Predicting the silver price for economic development is of great practical significance. However, the traditional time series prediction models have shortcomings, such as poor nonlinear fitting ability and low prediction accuracy. Therefore, this paper presents a novel hybrid model of CNN-SA-NGU for silver closing price prediction, which includes conventional neural networks (CNNs), the self-attention mechanism (SA), and the new gated unit (NGU). A CNN extracts the feature of input data. The SA mechanism captures the correlation between different eigenvalues, thus forming new eigenvectors to make weight distribution more reasonable. The NGU is a new deep-learning gated unit proposed in this paper, which is formed by a forgetting gate and an input gate. The NGU’s input data include the cell state of the previous time, the hidden state of the previous time, and the input data of the current time. The NGU learns the previous time’s experience to process the current time’s input data and adds a Tri module behind the input gate to alleviate the gradient disappearance and gradient explosion problems. The NGU optimizes the structure of traditional gates and reduces the computation. To prove the prediction accuracy of the CNN-SA-NGU, this model is compared with the thirteen other time series forecasting models for silver price prediction. Through comparative experiments, the mean absolute error (MAE) value of the CNN-SA-NGU model is 87.898771, the explained variance score (EVS) value is 0.970745, the r-squared (R2) value is 0.970169, and the training time is 332.777 s. The performance of CNN-SA-NGU is better than other models.