基于深度学习的中国黄金期货价格区间预测OA

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.