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In this study, we discuss various machine learning algorithms and architectures suitable for the Nigerian Naira exchange rate forecast. Our analyses were focused on the exchange rates of the British Pounds, US Dollars and the Euro against the Naira. The exchange rate data was sourced from the Central Bank of Nigeria. The performances of the algorithms were evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute Error and the coefficient of determination (R-Squared score). Finally, we compared the performances of these algorithms in forecasting the exchange rates.

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References

  1. I. Ozturk, “Exchange rate volatility and trade: A literature survey,” International Journal of Applied Econometrics and Quantitative Studies, vol. 3, no. 1, 2006.
     Google Scholar
  2. T. Caporale and K. Doroodian, “Exchange rate variability and the flow of international trade,” Economics Letters, vol. 46, no. 1, pp. 49–54, 1994.
     Google Scholar
  3. B. Coric ́ and G. Pugh, “The effects of exchange rate variability on international trade: a meta-regression analysis,” Applied Economics, vol. 42, no. 20, pp. 2631–2644, 2010.
     Google Scholar
  4. M. J. Bailey, G. S. Tavlas, and M. Ulan, “Exchange-rate variability and trade performance: evidence for the big seven industrial countries,” Review of World Economics, vol. 122, no. 3, pp. 466–477, 1986.
     Google Scholar
  5. Central Bank of Nigeria, “Cbn exchange rates,” September 2019. [Online]. Available: https://www.cbn.gov.ng/rates/ExchRateByCurrency.
     Google Scholar
  6. L. Kabari and B. Nwamae, “Stochastic analysis of the exchange rate of naira, yen, gbp, cfa and franc in relation to us dollar and predicting the naira for the year 2025,” European Journal of Engineering Research and Science, vol. 4, no. 6, pp. 15–18, Jun. 2019. [Online]. Available: https://www.ejers.org/index.php/ejers/article/view/1353
     Google Scholar
  7. E. H. Etuk, “Forecasting nigerian naira-us dollar exchange rates by a seasonal arima model,” American Journal of scientific research, vol. 59, pp. 71–78, 2012.
     Google Scholar
  8. R. F. Engle, “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation,” Econometrica: Journal of the Econometric Society, pp. 987–1007, 1982.
     Google Scholar
  9. T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of econometrics, vol. 31, no. 3, pp. 307–327, 1986.
     Google Scholar
  10. E. Karakoyun and A. Cibikdiken, “Comparison of arima time series model and lstm deep learning algorithm for bitcoin price forecasting,” in The 13th Multidisciplinary Academic Conference in Prague 2018 (The 13th MAC 2018), 2018, pp. 171–180.
     Google Scholar
  11. S. McNally, J. Roche, and S. Caton, “Predicting the price of bitcoin using machine learning,” in 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE, 2018, pp. 339–343.
     Google Scholar
  12. D. R. Pant, P. Neupane, A. Poudel, A. K. Pokhrel, and B. K. Lama, “Recurrent neural network based bitcoin price prediction by twitter sentiment analysis,” in 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS). IEEE, 2018, pp. 128–132.
     Google Scholar
  13. S. Lahmiri and S. Bekiros, “Cryptocurrency forecasting with deep learning chaotic neural networks,” Chaos, Solitons & Fractals, vol. 118, pp. 35–40, 2019.
     Google Scholar
  14. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vander- plas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
     Google Scholar
  15. L. Buitinck, G.Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. Vander Plas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: experiences from the scikit-learn project,” in ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 2013, pp. 108–122.
     Google Scholar
  16. C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.
     Google Scholar
  17. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
     Google Scholar