Comparative Analysis of Exchange Rate Forecasting Techniques: Emphasis on Machine Learning Algorithms for Pakistan
DOI:
https://doi.org/10.21015/vtse.v13i4.2183Abstract
The exchange rate is crucial because it can influence a country’s economy. It helps brokers and traders in operational decisions that help reduce risk and maximize profits. Many methods of forecasting currency exchange rates exist. The present study focused on different methodologies, including Box-Jenkins, Holt’s practice, artificial neural networks, Facebook Prophet Model, and Multilayer Perceptron (MLP) for predicting exchange rates. The performance of these techniques is evaluated based on small mean squared error, mean absolute error, and mean absolute percentage error. The results revealed that MLP outperformed all the models. It is a promising method to forecast the exchange rate of Pakistan because it gives a minor forecast error. In addition, the predicted values using MLP are very close to the actual values. The experimental results and time series plot revealed that the exchange rate of Pakistan will slightly increase in the upcoming months. It is concluded that the present study will help to determine the aggregate demand for domestic currency in the coming months. It is also helpful for the government and policymakers. However, understanding exchange rates is essential for anyone involved in international business and finance.
References
S. I. Abir, S. A. Al Shiam, R. M. Zakaria, A. H. Shimanto, S. S. Arefeen, M. S. A. Dolon, N. Sultana, and S. Shoha, “Use of AI-powered precision in machine learning models for real-time currency exchange rate forecasting in BRICS economies,” Journal of Economics, Finance and Accounting Studies, vol. 6, no. 6, pp. 66–83, 2024.
R. Aminudin and Y. Putra, “Poverty line forecasting model using double exponential smoothing Holt’s method,” IOP Conference Series: Materials Science and Engineering, vol. 662, p. 062007, 2019.
M. Arif, M. Abbas, M. A. Shehzad, Z. Batool, M. Rabia, and A. M. Soomro, “An ensembling approach to predict hepatitis in patients with liver disease using machine learning,” VFAST Transactions on Software Engineering, vol. 11, no. 3, pp. 42–52, 2023.
M. Arif, M. A. Aslam, H. U. Rehman, M. Abbas, and S. Bukhari, “Laboratory diagnostic pathways using machine learning,” VFAST Transactions on Software Engineering, vol. 10, no. 1, pp. 78–85, 2022.
A. Azadeh, S. F. Ghaderi, and S. Sohrabkhani, “A simulation-based neural network algorithm for forecasting electrical energy consumption in Iran,” Energy Policy, vol. 36, no. 7, pp. 2637–2644, 2008.
D. E. Babatunde, “Artificial neural network and its applications in the energy sector: An overview,” Tech. Rep. 670216917, 2020.
N. Badar, H. Fatima, A. Jabbar, and M. Asif, “Major food crops production and yield forecast in Pakistan,” Pakistan Journal of Agricultural Research, vol. 28, no. 3, 2015.
N. Barkhomay, “The exchange rate volatility and trade balance: Case of Uzbekistan,” Journal of Applied and Business Research (JAEBER), vol. 1, no. 3, pp. 149–161, 2011.
V. Bartoš, M. Vochozka, and V. Sanderová, “Copper and aluminium as economically imperfect substitutes, production and price development,” Acta Montan Slovaca, vol. 27, pp. 462–478, 2022.
P. H. Borghi, O. Zakordonets, and J. P. Teixeira, “A COVID-19 time series forecasting model based on MLP-ANN,” Procedia Computer Science, vol. 181, pp. 940–947, 2021.
G. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control, 3rd ed., Hoboken, NJ: Wiley, 1994.
G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control, Rev. ed., Englewood Cliffs, NJ: Prentice Hall, 1990.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014.
J. G. de Gooijer and R. J. Hyndman, “25 years of time series forecasting,” International Journal of Forecasting, vol. 22, no. 3, pp. 443–473, 2006.
S. Edwards, “Real exchange rates in the developing countries: Concepts and measurement,” Tech. Rep., 1989.
B. Eichengreen, “Real exchange rate and economic growth,” Social and Economic Studies, vol. 56, no. 4, pp. 7–20, 2008.
M. Elsaraiti and A. Merabet, “A comparative analysis of the ARIMA and LSTM predictive models and their effectiveness for predicting wind speed,” Energies, vol. 14, no. 20, p. 6782, 2021.
E. Erdogdu, “Natural gas demand in Turkey,” Applied Energy, vol. 87, no. 1, pp. 211–219, 2010.
I. A. Faheem-ul-Hussain Dehraj and M. A. Channa, “The impact of exchange rate fluctuation on imports and exports of Pakistan: Time series analysis from 1985–2015,” International Journal of Management Sciences and Business Research, 2017.
F. N. Fauziah, A. Gunaryati, and R. T. K. Sari, “Comparison forecasting with double exponential smoothing and artificial neural network to predict the price of sugar,” Int. J. Simul. Syst. Sci. Technol., vol. 18, no. 4, pp. 11–13, 2017.
R. Fullér, “Neural fuzzy systems,” Tech. Rep., 1995.
P. Gala and C. R. Lucinda, “Exchange rate misalignment and growth: Old and new econometric evidence,” Revista Economia, vol. 7, no. 4, pp. 165–187, 2006.
G. Goverdhan, S. Khare, and R. Manoov, “Time series prediction: Comparative study of ML models in the stock market,” Tech. Rep., 2022.
E. Y. Hamid and M. H. O. Abushaba, “Use of exponential Holt model and the Box-Jenkins methodology in predicting the time series of cement production in Sudan,” Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 12, no. 1, pp. –42, 2020.
J. V. Hansen, J. B. McDonald, and R. D. Nelson, “Time series prediction with genetic algorithm designed neural networks: An empirical comparison with modern statistical models,” Computational Intelligence, vol. 15, no. 3, pp. 171–185, 1999.
M. Irfan, M. Irfan, and M. Tahir, “Modeling the province-wise yield of rice crop in Pakistan using GARCH model,” International Journal of Science and Technology, vol. 1, no. 6, pp. 224–228, 2011.
P. S. Kalekar, “Time series forecasting using Holt-Winters exponential smoothing,” Tech. Rep., Kanwal Rekhi School of Information Technology, 4329008(13), pp. 1–13, 2004.
M. Khandil and I. Mirzaie, “The effects of exchange rate fluctuations on output and prices: Evidence from developing countries,” The Journal of Developing Areas, pp. 189–219, 2005.
A. J. Khan, P. Azim, and S. H. Syed, “The impact of exchange rate volatility on trade: A panel study on Pakistan’s trading partners,” The Lahore Journal of Economics, vol. 19, no. 1, p. 31, 2014.
M. Khashei and M. Bijari, “An artificial neural network (p, d, q) model for time series forecasting,” Expert Systems with Applications, vol. 37, no. 1, pp. 479–489, 2010.
M. Khashei, M. Bijari, and G. A. R. Ardali, “Improvement of autoregressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs),” Neurocomputing, vol. 72, no. 4–6, pp. 956–967, 2009.
P. R. Lane and G. M. Milesi-Ferretti, “External wealth, the trade balance, and the real exchange rate,” European Economic Review, vol. 46, no. 6, pp. 1049–1071, 2002.
C. Lee, Y. Sehwan, and J. Jongdae, “Neural network model versus SARIMA model in forecasting Korean stock price index (KOSPI),” Issues in Information Systems, vol. 8, no. 2, pp. 372–378, 2007.
S. Liang, T. Deng, A. Huang, N. Liu, and X. Jiang, “Energy consumption prediction using the GRU-MM Attention-LightGBM model with features of Prophet decomposition,” PLOS ONE, vol. 18, no. 1, p. e0277085, 2023.
S. Lima, A. M. Gonçalves, and M. Costa, “Time series forecasting using Holt-Winters exponential smoothing: An application to economic data,” AIP Conference Proceedings, vol. 2186, p. 090003, AIP Publishing LLC, 2019.
A. Malki, E.-S. Atlam, and I. Gad, “Machine learning approach of detecting anomalies and forecasting time-series of IoT devices,” Alexandria Engineering Journal, vol. 61, no. 11, pp. 8973–8986, 2022.
A. Meyler, G. Kenny, and T. Quinn, “Forecasting Irish inflation using ARIMA models,” Tech. Rep., 1998.
M. Navratil and A. Kolkova, “Decomposition and forecasting time series in the business economy using Prophet forecasting model,” Central European Business Review, vol. 8, no. 4, p. 26, 2019.
V. R. Prybutok, J. Yi, and D. Mitchell, “Comparison of neural network models with ARIMA and regression models for prediction of Houston’s daily maximum ozone concentrations,” European Journal of Operational Research, vol. 122, no. 1, pp. 31–40, 2000.
M. Qureshi, N. Ahmad, S. Ullah, and A. R. ul Mustafa, “Forecasting real exchange rate (REER) using artificial intelligence and time series models,” Heliyon, vol. 9, no. 5, 2023.
M. Rabia, Z. Rashid, F. Babar, and M. Abbas, “Forecasting of pesticide usage in Pakistan: An application of the univariate ARIMA model and artificial neural network,” Nativa, vol. 13, no. 1, 2025.
A. Rahamneh and A. A. Ali, “Using single and double exponential smoothing for estimating the number of injuries and fatalities resulted from traffic accidents in Jordan (1981–2016),” Middle-East Journal of Scientific Research, vol. 25, no. 7, pp. 1544–1552, 2017.
Z. Rowland, A. Blahova, and G. Peng, “Silver as a value keeper and wealth distributor during an economic recession,” Acta Montanistica Slovaca, vol. 26, no. 4, 2021.
E. Schöneburg, “Stock price prediction using neural networks: A project report,” Neurocomputing, vol. 2, no. 1, pp. 17–27, 1990.
H. K. Sharma, K. Kumari, and S. Kar, “Short-term forecasting of air passengers based on hybrid rough set and double exponential smoothing models,” Intelligent Automation and Soft Computing, vol. 25, no. 1, pp. 1–13, 2019.
M.-L. Shen, C.-F. Lee, H.-H. Liu, P.-Y. Chang, and C.-H. Yang, “An effective hybrid approach for forecasting currency exchange rates,” Sustainability, vol. 13, no. 5, p. 2761, 2021.
R. Siddiqui and N. Akhtar, “The impact of changes in exchange rate on prices: A case study of Pakistan,” The Pakistan Development Review, pp. 1059–1066, 1999.
C. E. Smith, “Exchange rate variation, commodity price variation and the implications for international trade,” Journal of International Money and Finance, vol. 18, no. 3, pp. 471–491, 1999.
V. Soloviev, N. Titov, and E. Smirnova, “Coking coal railway transportation forecasting using ensembles of ElasticNet, LightGBM, and Facebook Prophet,” in Machine Learning, Optimization, and Data Science: 6th International Conference (LOD 2020), Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part II, vol. 6, pp. 181–190, Springer, 2020.
S. J. Taylor and B. Letham, “Forecasting at scale,” The American Statistician, vol. 72, no. 1, pp. 37–45, 2018.
U. Thakur, S. K. Singh, S. Kumar, H. Singh, V. Arya, B. B. Gupta, R. W. Attar, A. Alhomoud, and K. T. Chui, “Advanced web traffic modelling and forecasting with a hybrid predictive approach,” Journal of Web Engineering, vol. 24, no. 3, pp. 409–456, 2025.
B. V. Vishwas and A. Patel, Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques, Apress, 2020.
M. Vochozka, F. Petrách, and S. Janek, “Changes in perception of coffee in EU: Luxury good turned inferior,” Economics & Sociology, vol. 15, no. 3, pp. 248–267, 2022.
J. Yao, C. L. Tan, and H.-L. Poh, “Neural networks for technical analysis: A study on KLCI,” International Journal of Theoretical and Applied Finance, vol. 2, no. 2, pp. 221–241, 1999.
G. Yu and Z. Schwartz, “Forecasting short time-series tourism demand with artificial intelligence models,” Journal of Travel Research, vol. 45, no. 2, pp. 194–203, 2006.
G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.
A. Riaz, N. R. Chaudhry, R. Choudhary, M. Riaz, and M. Suhail, “Capital Asset Pricing Model for the stock market in Pakistan,” Qlantic Journal of Social Sciences, vol. 5, no. 2, pp. 76–84, 2024.
B. Lim and S. Zohren, “Time-series forecasting with deep learning: A survey,” Philosophical Transactions of the Royal Society A, vol. 379, p. 20200209, 2021.
J. F. Torres, D. Hadjout, A. Sebaa, F. Martínez-Álvarez, and A. Troncoso, “Deep learning for time series forecasting: A survey,” Big Data, vol. 9, pp. 3–21, 2021.
R. P. Masini, M. C. Medeiros, and E. F. Mendes, “Machine learning advances for time series forecasting,” Journal of Economic Surveys, vol. 37, pp. 76–111, 2023.
K. Benidis, S. S. Rangapuram, V. Flunkert, Y. Wang, D. Maddix, C. Turkmen, et al., “Deep learning for time series forecasting: Tutorial and literature survey,” ACM Computing Surveys, vol. 55, pp. 1–36, 2022.
Z. Wang, F. Kong, S. Feng, M. Wang, X. Yang, H. Zhao, et al., “Is Mamba effective for time series forecasting?,” Neurocomputing, vol. 619, p. 129178, 2025.
A. Zeng, M. Chen, L. Zhang, and Q. Xu, “Are transformers effective for time series forecasting?,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 11121–11128, 2023.
R. Godahewa, C. Bergmeir, G. I. Webb, R. J. Hyndman, and P. Montero-Manso, “Monash time series forecasting archive,” arXiv preprint arXiv:2105.06643, 2021.
G. Shmueli and J. Polak, Practical Time Series Forecasting with R: A Hands-on Guide, Axelrod Schnall Publishers, 2024.
S. Lin, W. Lin, W. Wu, H. Chen, and J. Yang, “SparseTSF: Modeling long-term time series forecasting with 1k parameters,” arXiv preprint arXiv:2405.00946, 2024.
K. Yi, Q. Zhang, W. Fan, S. Wang, P. Wang, H. He, et al., “Frequency-domain MLPs are more effective learners in time series forecasting,” Advances in Neural Information Processing Systems, vol. 36, pp. 76656–76679, 2023.
X. Qiu, X. Li, R. Pang, Z. Pan, X. Wu, L. Yang, et al., “EasyTime: Time series forecasting made easy,” in 2025 IEEE 41st International Conference on Data Engineering (ICDE), pp. 4564–4567, 2025.
Z. Li, S. Qi, Y. Li, and Z. Xu, “Revisiting long-term time series forecasting: An investigation on linear mapping,” arXiv preprint arXiv:2305.10721, 2023.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY
