重庆工商大学学报（自然科学版）2026，Vol.43Issue(3)：135-143,9.DOI:10.16055/j.issn.1672-058X.2026.0003.016
基于深度学习的中国黄金期货价格区间预测
Interval Prediction of Chinese Gold Futures Prices Based on Deep Learning
摘要
Abstract
Objective To tackle the problem that the Chinese gold futures prices cannot be accurately predicted due to their inherent characteristics of non-linearity,instability,and high volatility,an interval prediction model based on deep learning is proposed.This model aims to more precisely depict and forecast the changing trends of gold futures prices.Methods First,three deep-learning models,namely the gated recurrent unit(GRU),long short-term memory(LSTM),and bidirectional long short-term memory(BiLSTM),were employed.Independent interval prediction models were constructed using the kernel density estimation(KDE)and quantile regression(QR)methods respectively.Then,based on these two types of interval prediction models,a combined prediction model was further integrated.Taking the prediction interval coverage rate as a constraint condition,the grid search(GS)algorithm was used to optimize the model weight configuration to minimize the average width of the prediction interval.Results Empirical analysis showed that the combined interval prediction model constructed using the BiLSTM method significantly outperformed other benchmark models in terms of prediction performance.At various confidence levels,it exhibited a wider prediction coverage rate and a narrower average interval width.Conclusion The proposed combined interval prediction model can more effectively predict the future fluctuation range of gold futures prices,providing more reliable risk assessment and decision-making support for investors.关键词
黄金期货/深度学习/核密度估计/分位数回归/区间预测Key words
gold futures/deep learning/kernel density estimation/quantile regression/interval prediction分类
信息技术与安全科学引用本文复制引用
李雨涵,孙景云..基于深度学习的中国黄金期货价格区间预测[J].重庆工商大学学报（自然科学版）,2026,43(3):135-143,9.基金项目
国家自然科学基金项目资助(72061020). （72061020）
