A Comparative Analysis of an Enhanced Hybrid Model for Predicting Dollar Against Naira Exchange Rate Using Deep Learning and Statistical Methods

Philip O. Odion; Maaruf M. Lawal; Abdulrashid Abdulrauf

doi:10.62411/jcta.12513

Authors

Philip O. Odion Nigerian Defence Academy
Maaruf M. Lawal Ahmadu Bello University
Abdulrashid Abdulrauf Federal Polytechnic Kaltungo

DOI:

https://doi.org/10.62411/jcta.12513

Keywords:

Deep learning, Exchange rate, Forecasting, FOREX, Hybrid machine learning, Statistical method

Abstract

In today’s global economy, accurately predicting foreign exchange rates or estimating their trends correctly is crucial for informed investment decisions. Despite the success of standalone models like ARIMA and deep learning models like LSTM, challenges persist in capturing both linear and nonlinear dynamics in highly volatile exchange rate environments. Motivated by the limitations of these individual models and the need for more robust forecasting tools, this study proposes a hybrid ARIMA-LSTM model that integrates ARIMA’s strength in modeling linear trends with LSTM’s capability to capture nonlinear dependencies, using historical USD/NGN exchange rate data from the Central Bank of Nigeria (CBN) spanning 2001 to 2024. The research hypothesis posits that the hybrid ARIMA-LSTM model will significantly outperform standalone models in forecasting accuracy. By comparing these models against state-of-the-art approaches, the study highlights the advantages of hybridizing statistical and deep learning methods. The findings demonstrate that the hybrid model achieved the lowest Root Mean Squared Error (RMSE) of 2.216 and the highest R² of 0.998, indicating superior forecasting performance. This study fills a critical research gap by demonstrating the effectiveness of hybrid deep learning in financial time series forecasting, providing valuable insights for investors, policymakers, and financial analysts. Future research will extend this work by incorporating the latest dataset and evaluating model robustness during the recent surge in the Naira/Dollar exchange rate from 2023 to 2024.

Author Biographies

Philip O. Odion, Nigerian Defence Academy

Department of Computer Science, Nigerian Defence Academy, Kaduna, Nigeria

Maaruf M. Lawal, Ahmadu Bello University

Department of Computer Science, Ahmadu Bello University, Kaduna State, Nigeria

Abdulrashid Abdulrauf, Federal Polytechnic Kaltungo

Department of Computer Science, Federal Polytechnic Kaltungo, Gombe State, Nigeria

References

O. Ajumi and A. Kaushik, “Exchange Rates Prediction via Deep Learning and Machine Learning: A Literature Survey on Currency Forecasting,” Int. J. Sci. Res., vol. 7, no. 12, pp. 1252–1262, 2018, [Online]. Available: www.ijsr.net

P. C. Rodrigues, O. O. Awe, J. S. Pimentel, and R. Mahmoudvand, “Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks,” Stats, vol. 3, no. 2, pp. 137–157, Jun. 2020, doi: 10.3390/stats3020012.

U. Ibekwe and L. Ajijola, “Modelling the Naira/Us Dollar Currency Exchange Rates Using Decision Tree, Ordinary Least Squares and Random Forest Machine Learning Algorithms,” UNILAG J. Bus., vol. 8, no. 2, pp. 53–74, 2022.

T. Nyoni, “Munich Personal RePEc Archive Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box-Jenkins ARIMA approach Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box-Jenkins ARIMA approach,” Munich Personal RePEc Archive. 2018. [Online]. Available: https://mpra.ub.uni-muenchen.de/88622/

J. A. Ingio, A. S. Nsang, and A. Iorliam, “Optimizing Rice Production Forecasting Through Integrating Multiple Linear Regression with Recursive Feature Elimination,” J. Futur. Artif. Intell. Technol., vol. 1, no. 2, pp. 96–108, Aug. 2024, doi: 10.62411/faith.2024-17.

A. Auclert, “Monetary Policy and the Redistribution Channel,” Am. Econ. Rev., vol. 109, no. 6, pp. 2333–2367, Jun. 2019, doi: 10.1257/aer.20160137.

P. Mueller, A. Tahbaz‐Salehi, and A. Vedolin, “Exchange Rates and Monetary Policy Uncertainty,” J. Finance, vol. 72, no. 3, pp. 1213–1252, Jun. 2017, doi: 10.1111/jofi.12499.

A. Abbate and M. Marcellino, “Point, Interval and Density Forecasts of Exchange Rates with Time Varying Parameter Models,” J. R. Stat. Soc. Ser. A Stat. Soc., vol. 181, no. 1, pp. 155–179, Jan. 2018, doi: 10.1111/rssa.12273.

J. P. Byrne, D. Korobilis, and P. J. Ribeiro, “On the sources of uncertainty in exchange rate predictability,” Int. Econ. Rev. (Philadelphia)., vol. 59, no. 1, pp. 329–357, Feb. 2018, doi: 10.1111/iere.12271.

Y.-W. Cheung, M. D. Chinn, A. G. Pascual, and Y. Zhang, “Exchange rate prediction redux: New models, new data, new currencies,” J. Int. Money Financ., vol. 95, pp. 332–362, Jul. 2019, doi: 10.1016/j.jimonfin.2018.03.010.

L. G. Kabari, M. B. Chigoziri, and J. Eneotu, “Machine Learning Algorithmic Study of the Naira Exchange Rate,” Eur. J. Eng. Res. Sci., vol. 5, no. 2, pp. 183–186, Feb. 2020, doi: 10.24018/ejers.2020.5.2.1739.

Z. Ibraeva, G. Bektemyssova, and A. R. Ahmad, “Fuzzy Model for Time Series Forecasting,” Sci. J. Astana IT Univ., pp. 93–102, Mar. 2023, doi: 10.37943/13EOTU7482.

A. F. Adekoya, I. K. Nti, and B. A. Weyori, “Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi,” FinTech, vol. 1, no. 1, pp. 25–43, Dec. 2021, doi: 10.3390/fintech1010002.

P. O. Junior, G. Tweneboah, and A. M. Adam, “Interdependence of Major Exchange Rates in Ghana: A Wavelet Coherence Analysis,” J. African Bus., vol. 20, no. 3, pp. 407–430, Jul. 2019, doi: 10.1080/15228916.2019.1583973.

L. Bulut, “Google Trends and the forecasting performance of exchange rate models,” J. Forecast., vol. 37, no. 3, pp. 303–315, Apr. 2018, doi: 10.1002/for.2500.

P. Hewage et al., “Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station,” Soft Comput., vol. 24, no. 21, pp. 16453–16482, Nov. 2020, doi: 10.1007/s00500-020-04954-0.

W. Zhang, J. Li, and Y. Li, “A Novel Loss Function Considering the Distance Between Forecasting and Historical Values in Financial Time Series Forecasting Models,” in Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8–10, 2023, Guangzhou, China, 2024. doi: 10.4108/eai.8-12-2023.2344823.

M. Khashei, M. Bijari, and S. R. Hejazi, “Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting,” Soft Comput., vol. 16, no. 6, pp. 1091–1105, Jun. 2012, doi: 10.1007/s00500-012-0805-9.

K. A. Abimbola, O. A. Abiola, and S. O. Akinola, “Predicting the Trend of Dollar/Naira Exchange Rate Using Regression,” J. Sci. Logics ICT Res., vol. 8, no. 2, pp. 51–59, 2022.

C. I. Ugoh, L. B. Jammeh, M. N. Ugo, E. K. Guobadia, and C. S. Ngene, “Modeling and Forecasting Nigerian Naira/US Dollar and The Gambian Dalasi/US Dollar Exchange Rates: A Comparative Study,” African J. Math. Stat. Stud., vol. 6, no. 1, pp. 12–26, Feb. 2023, doi: 10.52589/AJMSS-XHLDL3XG.

R. Mbato and K. L. G., “Predicting Foreign Exchange Using Digital Signal Processing,” Br. J. Comput. Netw. Inf. Technol., vol. 4, no. 2, pp. 1–11, Sep. 2021, doi: 10.52589/BJCNIT-SQWFNRND.

I. Ukabuiro and A. Stella, “Prediction Models for Forex Data Exchange System,” Int. J. Innov. Sci. Res. Technol., vol. 8, no. 12, pp. 1725–1728, 2023, doi: 10.5281/zenodo.10453255.

T. Mahmud, T. Akter, S. Anwar, M. T. Aziz, M. S. Hossain, and K. Andersson, “Predictive Modeling in Forex Trading: A Time Series Analysis Approach,” in 2024 Second International Conference on Inventive Computing and Informatics (ICICI), Jun. 2024, pp. 390–397. doi: 10.1109/ICICI62254.2024.00070.

B. Magaji and J. Garba, “Forecasting the exchange rate of Nigerian Naira to United State’ Dollar using ARIMA-GARCH Model,” Dutse J. Pure Appl. Sci., vol. 8, no. 3b, pp. 87–96, Oct. 2022, doi: 10.4314/dujopas.v8i3b.9.

O. J. Olawale and D. J. Adashu, “A Combination of ARIMA Models and Neural Networks in Forecasting Nigerian Exchange Rate,” African Multidiscip. J. Sci. Artif. Intell., vol. 1, no. 1, pp. 14–32, 2024, doi: 10.58578/amjsai.v1i1.3367.

Central Bank of Nigeria, “Exchange Rates,” Central Bank of Nigeria, 2025. https://www.cbn.gov.ng/rates/ExchRateByCurrency.html

P. L. Seabe, C. R. B. Moutsinga, and E. Pindza, “Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach,” Fractal Fract., vol. 7, no. 2, p. 203, Feb. 2023, doi: 10.3390/fractalfract7020203.

S. Adamu, A. Iorliam, and Ö. Asilkan, “Exploring Explainability in Multi-Category Electronic Markets: A Comparison of Machine Learning and Deep Learning Approaches,” J. Futur. Artif. Intell. Technol., vol. 1, no. 4, pp. 440–454, Mar. 2025, doi: 10.62411/faith.3048-3719-58.