| 引用本文: | 李雨涵1,孙景云1,2.基于深度学习的中国黄金期货价格区间预测(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（3）：135-143 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| :目的 为解决中国黄金期货价格由于固有的非线性、不稳定和高波动性特征而无法精确预测的问题,提出了
一个基于深度学习的区间预测模型,以更精确地描绘和预测黄金期货价格的变化趋势。 方法 首先,采用门控循环
单元(GRU)、长短期记忆(LSTM)和双向长短期记忆(BiLSTM)3 种深度学习模型,分别通过核密度估计(KDE)和
分位数回归(QR)方法构建独立的区间预测模型;然后,基于这两种区间预测模型,进一步整合成一个组合预测模
型,以预测区间覆盖率作为约束条件,并利用网格搜索算法(GS)来优化模型权重配置,以确保预测区间的平均宽
度达到最小。 结果 实证分析显示:采用 BiLSTM 方法构建的组合区间预测模型在预测性能上显著优于其他基准模
型,在各种置信水平下,都显示出更广泛的预测覆盖率和更窄的区间平均宽度。 结论 所提出的区间预测组合模型
能够有效预测未来黄金期货价格的波动范围,为投资者提供更为可靠的风险评估和决策支持。 |
| 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 |
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| Interval Prediction of Chinese Gold Futures Prices Based on Deep Learning |
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LI Yuhan1 SUN Jingyun1 2
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1. School of Statistics and Data Science Lanzhou University of Finance and Economics Lanzhou 730020 China
2. Gansu Research Center for Quantitative Analysis of Economic Development Lanzhou 730020 China
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| 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
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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. |
| Key words: gold futures deep learning kernel density estimation quantile regression interval prediction |
