Forecasting the EUR/USD exchange rate using EEMD in combination with LSTM Algorithm


Authors

  • Tran Thi Tuan Anh University of Economics Ho Chi Minh City
  • Nguyen Cong Quoc University of Economics Ho Chi Minh City
DOI: https://doi.org/10.57110/vnu-jeb.v4i3.294

Keywords:

EUR/USD exchange rate, ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM)

Abstract

Predicting currency exchange rates, particularly for major currencies such as USD and EUR, poses considerable difficulty owing to the complex nature of financial temporal data. This paper utilizes a combined approach that merges the ensemble empirical mode decomposition (EEMD) technique with the long short-term memory (LSTM) neural network to anticipate the sequence of EUR/USD exchange rates. In this fusion method, the EUR/USD rate is decomposed into several intrinsic mode functions (IMFs), which serve as inputs for the LSTM network to perform predictive analysis. The forecasted exchange rate is derived by aggregating the predicted values of these IMFs. Validation results demonstrate that the EEMD-LSTM combined model significantly outperforms in predicting the closing price of the EUR/USD exchange rate. This finding highlights the potential of the EEMD-LSTM combined algorithm in forecasting other complex financial series.

References

Bukowski, S. I., & Bukowska, J. E. (2017). Financial and fiscal crises, prices and EUR/USD rate of exchange. International Journal of Business and Economic Sciences Applied Research (IJBESAR), 10(3). http://doi.org/10.25103/ijbesar.103.05

Bussiere, M. (2013). Exchange rate pass‐through to trade prices: The role of nonlinearities and asymmetries. Oxford Bulletin of Economics and Statistics, 75(5), 731-758. http://doi.org/10.1111/j.1468-0084.2012.00711.x

Chen, Z., Yuan, C., Wu, H., Zhang, L., Li, K., Xue, X., & Wu, L. (2022). An improved method based on EEMD-LSTM to predict missing measured data of structural sensors. Applied Sciences, 12(18), 9027. https://doi.org/10.3390/app12189027

Dobrovolny, M., Soukal, I., Lim, K. C., Selamat, A., & Krejcar, O. (2020). Forecasting of FOREX price trend using recurrent neural network-long short-term memory. Conference: Hradec Economic Days 2020. http://doi.org/10.36689/uhk/hed/2020-01-011

Galeshchuk, S., & Mukherjee, S. (2017). Deep networks for predicting direction of change in foreign exchange rates. Intelligent Systems in Accounting, Finance and Management, 24(4), 100-110. http://doi.org/10.1002/isaf.1404

Huang, N. E. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, physical and engineering sciences. https://doi.org/10.1098/rspa.1998.0193

Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522. https://doi.org/10.1016/j.cor.2004.03.016

Janetzko, D. (2014). Using Twitter to model the EUR/USD exchange rate. arXiv preprint arXiv:1402.1624. https://doi.org/10.48550/arXiv.1402.1624

Özorhan, M. O., Toroslu, İ. H., & Şehitoğlu, O. T. (2017). A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms. Soft Computing, 21, 6653-6671. https://doi.org/10.1007/s00500-016-2216-9

Pérez-Rodríguez, J. V. (2006). The Euro and other major currencies floating against the U.S. dollar. Atlantic Economic Journal, 34, 367-384. https://doi.org/10.1007/s11293-006-9042-x

Qu, Y., & Zhao, X. (2019). Application of LSTM neural network in forecasting foreign exchange price. Journal of Physics: Conference Series. http://doi.org/10.1088/1742-6596/1237/4/042036

Ribeiro, G. A. M. (2016). Macroeconomic determinants of international currencies. Master’s thesis.

Shen, F., Chao, J., & Zhao, J. (2015). Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing, 167, 243-253. https://doi.org/10.1016/j.neucom.2015.04.071

Ulina, M., Purba, R., & Halim, A. (2020). Foreign exchange prediction using CEEMDAN and improved FA-LSTM. The 2020 Fifth International Conference on Informatics and Computing (ICIC). https://doi.org/10.1109/ICIC50835.2020.9288615

Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53, 5929-5955. https://doi.org/10.1007/s10462-020-09838-1

Vukovic, D., & VYKLYUK, V. (2013). Forex predicton with neural network: usd/eur currency pair. Actual Problems of Economics, 10, 251-261.

Wu, Y. X., Wu, Q. B., & Zhu, J. Q. (2019). Improved EEMD-based crude oil price forecasting using LSTM networks. Physica A: Statistical Mechanics and its Applications, 516, 114-124. https://doi.org/10.1016/j.physa.2018.09.120

Yan, B., & Aasma, M. (2020). A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM. Expert systems with Applications, 159, 113609.8, 71206-71218. https://doi.org/10.1016/j.eswa.2020.113609

Yao, J., & Tan, C. L. (2000). A case study on using neural networks to perform technical forecasting of forex. Neurocomputing, 34(1-4), 79-98. https://doi.org/10.1016/S0925-2312(00)00300-3

Zhang, B. (2018, July). Foreign exchange rates forecasting with an EMD-LSTM neural networks model. Journal of Physics: Conference Series, 1053(1), p. 012005. http://doi.org/10.1088/1742-6596/1053/1/012005

Downloads

Download data is not yet available.

Additional Files

Published

25-06-2024

Abstract View

214

PDF Downloaded

0

How to Cite

Tran Thi Tuan Anh, & Nguyen Cong Quoc. (2024). Forecasting the EUR/USD exchange rate using EEMD in combination with LSTM Algorithm. VNU JOURNAL OF ECONOMICS AND BUSINESS, 4(3), 44. https://doi.org/10.57110/vnu-jeb.v4i3.294

Issue

Vol. 4 No. 3

Section

Original Article

License

Copyright (c) 2024 Tran Thi Tuan Anh, Nguyen Cong Quoc

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

by VNU Journal of Economics and Business