Study on Exchange Rate Forecasting with Stacked Optimization Based on a Learning Algorithm

Weiwei Xie, Haifeng Wu, Boyu Liu (), Shengdong Mu and Nedjah Nadia
Additional contact information
Weiwei Xie: School of Public Administration, Central China Normal University, Wuhan 430079, China
Haifeng Wu: Sustainable Finance Research Center, Shenzhen Institute of Data Economy Research Fellow of Shenzhen Finance Institute, Shenzhen 518172, China
Boyu Liu: School of Innovation and Entrepreneurship, Hubei University of Economics, Wuhan 430205, China
Shengdong Mu: Collaborative Innovation Center of Green Development in the Wuling Shan Region, Yangtze Normal University, Fuling, Chongqing 408100, China
Nedjah Nadia: Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 205513, Brazil

Mathematics, 2024, vol. 12, issue 4, 1-20

Abstract: The time series of exchange rate fluctuations are characterized by non-stationary and nonlinear features, and forecasting using traditional linear or single-machine models can cause significant bias. Based on this, the authors propose the combination of the advantages of the EMD and LSTM models to reduce the complexity by analyzing and decomposing the time series and forming a new model, EMD-LSTM-SVR, with a stronger generalization ability. More than 30,000 units of data on the USD/CNY exchange rate opening price from 2 January 2015 to 30 April 2022 were selected for an empirical demonstration of the model’s accuracy. The empirical results showed that the prediction of the exchange rate fluctuation with the EMD-LSTM-SVR model not only had higher accuracy, but also ensured that most of the predicted positions deviated less from the actual positions. The new model had a stronger generalization ability, a concise structure, and a high degree of ability to fit nonlinear features, and it prevented gradient vanishing and overfitting to achieve a higher degree of prediction accuracy.

Keywords: LSTM; EMD; optimization; exchange rate prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/4/614/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/4/614/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:4:p:614-:d:1341536

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().