Shanghai gold price data table.

Accurate prediction of gold prices is crucial for investment decision-making and national risk management. The time series data of gold prices exhibits random fluctuations, non-linear characteristics, and high volatility, making prediction extremely challenging. Various methods, from classical statistics to machine learning techniques like Random Forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), have achieved high accuracy, but they also have inherent limitations. To address these issues, a model that combines Temporal Convolutional Networks (TCN) with Query (Q) and Keys (K) attention mechanisms (TCN-QV) is proposed to enhance the accuracy of gold price predictions. The model begins by employing stacked dilated causal convolution layers within the TCN framework to effectively extract temporal features from the sequence data. Subsequently, an attention mechanism is introduced to enable adaptive weight distribution according to the information features. Finally, the predicted results are generated through a dense layer. This method is used to predict the time series data of gold prices in Shanghai. The optimized model demonstrates a substantial improvement in Mean Absolute Error (MAE) compared to the baseline model, achieving reductions of approximately 5.47% in the least favorable case and up to 33.69% in the most favorable scenario across four experimental datasets. Additionally, the model is tested across different time steps and shows satisfactory performance in long sequence predictions. To validate the necessity of the model components, this paper conducts ablation experiments to confirm the significance of each segment.精准预测黄金价格，对投资决策与国家风险管理均具有重要意义。黄金价格时序数据呈现随机波动、非线性特征与高波动性，使得预测工作极具挑战性。从经典统计方法到随机森林（Random Forests）、卷积神经网络（Convolutional Neural Networks，CNN）、循环神经网络（Recurrent Neural Networks，RNN）等机器学习技术，各类方法均已取得较高预测精度，但同时也存在固有局限。针对上述问题，本文提出一种将时间卷积网络（Temporal Convolutional Networks，TCN）与查询（Query，Q）、键（Keys，K）注意力机制相结合的模型（TCN-QV），以提升黄金价格预测的精度。该模型首先在TCN框架内采用堆叠膨胀因果卷积层，有效提取时序数据中的时间特征；随后引入注意力机制，使其可根据信息特征实现自适应权重分配；最终借助全连接层生成预测结果。本文将该方法应用于上海黄金价格时序数据的预测任务。相较于基准模型，优化后的模型在平均绝对误差（Mean Absolute Error，MAE）指标上实现显著提升：在四个实验数据集上，最差场景下误差降低约5.47%，最优场景下误差降幅可达33.69%。此外，本文针对不同时间步长对该模型开展测试，结果显示其在长序列预测任务中表现良好。为验证模型各组件的必要性，本文通过消融实验证实了各模块的重要性。