A Novel Gold Futures Price Prediction Model based on PCA-AGRU

As an important financial investment commodity, the price fluctuations of gold significantly impact the global economy and the stability of the financial market. Therefore, it is of great significance to accurately predict the price of gold futures. This paper presents a novel gold futures price prediction model, PCA-AGRU, based on Principal Component Analysis (PCA) and Adaptive Gated Recurrent Unit (AGRU). PCA is utilized for the dimensionality reduction of data while extracting essential features. AGRU bases on Gated Recurrent Unit (GRU), embeds Self-Attention (SA), and adds an adaptive adjustment mechanism, making the model more effective in capturing long-term dependencies within time series data. This paper uses the international gold futures market data as the experimental dataset, and uses Maximal Information Coefficient (MIC) to analyze the correlation of the influencing factors. The PCA-AGRU model is compared with five prediction models of GRU, SA-GRU, AGRU, PCA-GRU, and PCA-SA-GRU. The experimental results show that the PCA-AGRU model performs best in forecasting gold futures prices.