Meng, S.; Chen, A.; Wang, C.; Zheng, M.; Wu, F.; Chen, X.; Ni, H.; Li, P. Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models. Preprints2024, 2024100812. https://doi.org/10.20944/preprints202410.0812.v1
APA Style
Meng, S., Chen, A., Wang, C., Zheng, M., Wu, F., Chen, X., Ni, H., & Li, P. (2024). Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models. Preprints. https://doi.org/10.20944/preprints202410.0812.v1
Chicago/Turabian Style
Meng, S., Haowei Ni and Panfeng Li. 2024 "Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models" Preprints. https://doi.org/10.20944/preprints202410.0812.v1
Abstract
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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
