A CEEMDAN-LSTM Model for Forecasting the USD/DZD Exchange Rate

Abdelkader Sahed; Mohammed Mekidiche; Hacen Kahoui

doi:10.63356/ace.2026.003

Authors

Abdelkader Sahed University Centre of Maghnia, Department of Economics https://orcid.org/0009-0003-2776-0448
Mohammed Mekidiche University Centre of Maghnia, Department of Economics https://orcid.org/0000-0001-5001-9250
Hacen Kahoui University Centre of Maghnia, Department of Economics https://orcid.org/0000-0003-1760-2940

DOI:

https://doi.org/10.63356/ace.2026.003

Keywords:

CEEMDAN, LSTM, exchange rate forecasting, financial time series, deep learning, risk management

Abstract

This study develops a hybrid forecasting model for the USD/DZD exchange rate by combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long Short-Term Memory (LSTM) networks to address high volatility and complex temporal dependencies in currency markets. Using 310 monthly observations, the CEEMDAN procedure decomposes the series into five frequency components and a residual, which are modeled by component-specific LSTM networks. The proposed CEEMDAN-LSTM model achieved the lowest forecast errors among the tested models, with a MAPE of 0.4782%, outperforming traditional LSTM and SVM benchmarks. The 12-month forecast suggests relative exchange rate stability, with a slight decline of about 0.48%. The results indicate that decomposing the original series before LSTM modeling improves predictive accuracy by separating short-term noise from medium- and long-term dynamics. The proposed framework may support exchange-rate risk management, hedging decisions, and short- to medium-term planning in emerging-market settings.

References

Adesina, O. S., & Obokoh, L. O. (2025). A hybrid framework of deep learning and traditional time series models for exchange rate prediction. Scientific African, 29, Article e02818. https://doi.org/10.1016/j.sciaf.2025.e02818

Al-Gounmeein, R. S., & Ismail, M. T. (2020). Forecasting the exchange rate of the Jordanian dinar versus the US dollar using a Box-Jenkins seasonal ARIMA model. International Journal of Mathematics and Computer Science, 15(1), 27–40. http://ijmcs.future-in-tech.net

Amri, I. F., Yunanita, N., Lestari, F. A., & Dhani, O. R. (2025). Rupiah exchange rate prediction against the US dollar using a deep neural network with a multi-output sliding window approach. MethodsX. Advance online publication. https://doi.org/10.1016/j.mex.2025.103692

Cao, J., Li, Z., & Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and Its Applications, 519, 127–139. https://doi.org/10.1016/j.physa.2018.11.061

Darvas, Z., & Schepp, Z. (2025). Forecasting the daily exchange rate of the UK pound sterling against the US dollar. Finance Research Letters, 71, Article 106451. https://doi.org/10.1016/j.frl.2024.106451

De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3), 443–473. https://doi.org/10.1016/j.ijforecast.2006.01.001

Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263. https://doi.org/10.1080/07350015.1995.10524599

Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall/CRC. https://doi.org/10.1007/978-1-4899-4541-9

Fan, W., Song, M., & Du, Z. (2021). Forecasting carbon price based on CEEMDAN and LSTM optimized by whale optimization algorithm. Journal of Forecasting, 40(7), 1235–1250. https://doi.org/10.1002/for.2761

Flores-Sosa, M., León-Castro, E., Merigó, J. M., & Yager, R. R. (2022). Forecasting the exchange rate with multiple linear regression and heavy ordered weighted average operators. Knowledge-Based Systems, 248, Article 108863. https://doi.org/10.1016/j.knosys.2022.108863

García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2024). Foreign exchange forecasting models: LSTM and BiLSTM comparison. Engineering Proceedings, 68(1), Article 19. https://doi.org/10.3390/engproc2024068019

García Nieto, P.J., Sánchez Lasheras, F., García-Gonzalo, E., & de Cos Juez, F.J. (2018). PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study. Science of the Total Environment, 621, 753-761. https://doi.org/10.1016/j.scitotenv.2017.11.291

Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10), 2451-2471. https://doi.org/10.1162/089976600300015015

Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Huang, N. E., Shen, Z., & Long, S. R. (1999). A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics, 31(1), 417-457. https://doi.org/10.1146/annurev.fluid.31.1.417

Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193

Kamouh, M. M. N. (2026). Forecasting exchange rates using artificial neural networks: An applied study on the Arab Republic of Egypt. Arab Journal of Administration, 46(6), 1–22. https://doi.org/10.21608/aja.2024.331953.1740

Li, X. (2006). Temporal structure of neuronal population oscillations with empirical mode decomposition. Physics Letters A, 356(3), 237-241. https://doi.org/10.1016/j.physleta.2006.03.045

Lin, C.-S., Chiu, S.-H., & Lin, T.-Y. (2012). Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting. Economic Modelling, 29(6), 2583–2590. https://doi.org/10.1016/j.econmod.2012.07.018

Lin, Y., Yan, Y., Xu, J., Liao, Y., & Ma, F. (2021). Forecasting stock index price using the CEEMDAN-LSTM model. The North American Journal of Economics and Finance, 57, 101421. https://doi.org/10.1016/j.najef.2021.101421

Lin, Y., Liao, Q., Lin, Z., Tan, B., & Yu, Y. (2022). A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction. Resources Policy, 78, 102884. https://doi.org/10.1016/j.resourpol.2022.102884

Liu, P., Wang, Z., Liu, D., Wang, J., & Wang, T. (2023). A CNN-STLSTM-AM model for forecasting USD/RMB exchange rate. Journal of Engineering Research, 11(3), Article 100079. https://doi.org/10.1016/j.jer.2023.100079

Lyócsa, Š., Plíhal, T., & Výrost, T. (2024). Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility. International Journal of Forecasting, 40(4), 1275–1301. https://doi.org/10.1016/j.ijforecast.

11.003

Manowska, A. (2020). Using the LSTM Network to Forecast the Demand for Electricity in Poland. Applied Sciences, 10(23), 8455. https://doi.org/10.3390/app10238455

Plakandaras, V., Papadimitriou, T., & Gogas, P. (2015). Forecasting daily and monthly exchange rates with machine learning techniques. Journal of Forecasting, 34(7), 560–573. https://doi.org/10.1002/for.2354

Ren, Y., Suganthan, P. N., & Srikanth, N. (2016). Ensemble methods for wind and solar power prediction - A state-of-the-art review. Renewable and Sustainable Energy Reviews, 50, 82–91. https://doi.org/10.1016/j.rser.2015.04.081

Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4144–4147. https://doi.org/10.1109/ICASSP.2011.5947265

Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. Springer, New York.

https://doi.org/10.1007/978-1-4757-2440-0

Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1), 1–41. https://doi.org/10.1142/S1793536909000047

Yu, L., Wang, S., & Lai, K. K. (2008). Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30(5), 2623-2635. https://doi.org/10.1016/j.eneco.2008.05.003

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