用深度学习提升人民币兑美元汇率预测准确率，还让模型更易懂。
Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models
- 用LSTM、CNN和TSMixer等模型预测汇率，选了40个经济特征
- TSMixer表现最好，准确率优于传统方法
- 结合可视化技术解释模型决策，适合金融分析人员使用
准确的汇率预测对金融稳定和国际贸易至关重要，是经济与金融研究的核心课题。传统模型在应对汇率数据内在复杂性和非线性时往往表现不佳。本研究探索了LSTM、CNN及基于Transformer的架构在人民币兑美元汇率预测中的应用，利用涵盖6大类共40个特征的数据集，发现TSMixer为最优模型。通过严格的特征筛选，强调了中美贸易额、欧元兑人民币、日元兑美元等关键经济指标的重要性。结合grad-CAM可视化技术，显著提升了模型可解释性，有助于识别最具影响力的变量，增强预测可信度。研究证实基础经济数据在汇率预测中的核心作用，并展示了机器学习模型在提供更精准、可靠预测方面的巨大潜力，可为金融分析与决策提供有力支持。
原文摘要 · Abstract (English)
Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic and financial research. Traditional forecasting models often falter when addressing the inherent complexities and non-linearities of exchange rate data. This study explores the application of advanced deep learning models, including LSTM, CNN, and transformer-based architectures, to enhance the predictive accuracy of the RMB/USD exchange rate. Utilizing 40 features across 6 categories, the analysis identifies TSMixer as the most effective model for this task. A rigorous feature selection process emphasizes the inclusion of key economic indicators, such as China-U.S. trade volumes and exchange rates of other major currencies like the euro-RMB and yen-dollar pairs. The integration of grad-CAM visualization techniques further enhances model interpretability, allowing for clearer identification of the most influential features and bolstering the credibility of the predictions. These findings underscore the pivotal role of fundamental economic data in exchange rate forecasting and highlight the substantial potential of machine learning models to deliver more accurate and reliable predictions, thereby serving as a valuable tool for financial analysis and decision-making.
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