政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/160200

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政大機構典藏 > 商學院 > 金融學系 > 期刊論文 > Item 140.119/160200

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/160200

Title:	Application of a Dual‑Stream Hybrid Network for Exchange Rate Prediction
Authors:	林建秀 Lin, Chien-Hsiu;Chen, Si-Qi;Liao, Szu-Lang
Contributors:	金融系
Keywords:	Exchange rate prediction;Convolutional long short-term memory;Hybrid neural network;Dual-stream architecture
Date:	2025-05
Issue Date:	2025-11-14
Abstract:	Exchange rate prediction has consistently been a popular research topic in the financial domain. In 2020, in response to the COVID-19 pandemic, the United States implemented large-scale quantitative easing policies. However, in 2022, to address domestic inflation, the United States began a series of significant interest rate hikes. Under these circumstances, the exchange rates of various currencies have experienced substantial fluctuations. In this study, we propose a novel hybrid model based on the Convolutional Long Short-Term Memory (CNN-LSTM) model, combined with a Residual Network (ResNet), aiming to improve the accuracy of exchange rate predictions. By integrating signal processing techniques such as Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Savitzky-Golay (SG) filters with an innovative dual-stream (DS) architecture, our model (DS-ResNet-LSTM) demonstrates outstanding performance across multiple metrics, significantly outperforming the traditional LSTM, CNN-LSTM and others. The experimental results indicate that the DS-ResNet-LSTM model exhibits strong robustness, high generalization capability, and clear advantages in numerical prediction, demonstrating its potential in financial time series analysis.
Relation:	Computational Economics
Data Type:	article
DOI 連結:	https://doi.org/10.1007/s10614-025-10957-6
DOI:	10.1007/s10614-025-10957-6
Appears in Collections:	[金融學系] 期刊論文

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