基于BO-XGBoost-PSO-TCN-BiGRU的黄金期货价格预测  

The Prediction of Gold Futures Prices Based on BO-XGBoost-PSO-TCN-BiGRU

作　　者：谷爱玲 莫俊铭 黄绮晴 

机构地区：[1]广东工业大学数学与统计学院,广东 广州 [2]五邑大学数学与计算科学学院,广东 江门

出　　处：《统计学与应用》2024年第6期2249-2260,共12页Statistical and Application

基　　金：国家自然科学基金(71971070, 71903036)。

摘　　要：为了充分挖掘影响金融数据中时序性特征联系,提高预测黄金期货价格的预测精度,提出了一种基于粒子群优化(PSO)、时间卷积神经网络(TCN)和双向门控循环单元(BiGRU)模型相结合的优化双智能体深度学习模型的黄金期货价格预测方法。选择2018~2024年交易日成交价,并添加六大类影响因素数据。通过贝叶斯优化XGBoost并结合SHAP算法进行可解释性特征筛选,再建立PSO-TCN-BiGRU混合模型进行预测,并通过与CNN、BiGRU、TCN、BiGRU-TCN、CNN-BiGRU、TCN-BiGRU模型对比分析,结果表明所提预测模型的RMSE值、MSE值、MAE值都低于其他模型,R方值最高,能更准确地预测黄金期货价格走势。The proposed optimized dual-agent deep learning model, based on particle swarm optimization (PSO), time convolutional neural network (TCN), and bidirectional gated recurrent unit (BiGRU) model, aims to fully explore the temporal characteristic relationships affecting financial data and enhance the prediction accuracy of gold futures price. The trading prices of 2018~2024 trading days were selected, along with six categories of influencing factor data. Bayesian optimization XGBoost combined with SHAP algorithm was used to screen interpretability features, followed by the establishment of a PSO-TCN-BiGRU mixed model for prediction. Comparison and analysis with other models such as CNN, BiGRU, TCN, BiGRU-TCN, CNN-BiGRU, TCN-BiGRU models revealed that the proposed forecasting model exhibited lower RMSE value, MSE value and MAE value than other models while achieving the highest R-square value. This indicates its ability to predict the trend of gold futures price more accurately.

关 键 词：黄金期货价格 BO-XGBoost PSO-TCN-BiGRU 可解释性分析 混合模型 

分 类 号：F83[经济管理—金融学]