FORECASTING CURRENT EXCHANGE AND GOLD RATES WITH HYBRID MODELS USING TIME SERIES AND DEEP LEARNING ALGORITHMS

Year 2025, Volume: 9 Issue: 2, 171 - 182, 30.08.2025

Gökalp Çınarer

https://doi.org/10.46519/ij3dptdi.1633193

Abstract

The flexibility and volatility experienced in exchange rates affect many financial activities in the world. In order to follow this situation, countries and multinational companies need to follow financial indicators in the world economy. There is a need for important decision systems that will follow all these systems with a reliable prediction model together with the developing technology. In this study, deep learning-based models that will accurately predict the movement of gold, dollar, and euro exchange rates are proposed. Time Series methods were used to analyze the data and make predictions. In addition to deep learning models such as LSTM, GRU, Bi-LSTM and RNN, hybrid models of these methods were also used to compare their prediction performances. The data set includes USD/TRY and EUR/TRY exchange rates and monthly prices of bullion gold in Turkish Lira. Data between the years 2000-2024 were included in the analyses, and a six-month future prediction of each rate was also made. In the study, the best results were obtained with a 98.39% f1 score in gold rate prediction with the GRU-RNN hybrid model. It was observed that hybrid models in particular provided higher accuracy in predictions in general. The findings show that optimizing model parameters has a significant impact on the success of financial forecasts.

Keywords

Deep Learning , Time Series Models , Artificial Intelligence , Foreign Exchange Prediction , Hybrid Models , Data Mining

References

• 1. Yasar, H., and Kilimci, Z. H., "US dollar/Turkish lira exchange rate forecasting model based on deep learning methodologies and time series analysis", Symmetry, Vol. 12, Issue. 9, Pages 1553, 2020.
• 2. Duygulu, A. A., "Evaluation of Exchange Rate Stability in Terms of Economic Stability", Dokuz Eylul University Journal of Faculty of Economics and Administrative Sciences, Vol. 13, Issue. 1, Pages 105-116, 1998.
• 3. Elmastaş Gültekin, Ö., and Aktürk Hayat, E., "Analysis of Factors Affecting Gold Prices with VAR Model: 2005-2015 Period", Ege Academic Review, Vol. 16, Issue. 4, 2016.
• 4. Güney, S., and Ilgın, K. S., "Evaluation of the Impact of Investment Instruments on the BIST-100 Index", Erciyes University Journal of Faculty of Economics and Administrative Sciences, Issue. 53, Pages 226-245, 2019.
• 5. Manjula, K. A., and Karthikeyan, P., "Gold price prediction using ensemble based machine learning techniques", 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Pages 1360-1364, IEEE, April 2019.
• 6. Kilimci, H., Yıldırım, M., and Kilimci, Z. H., "The prediction of short-term bitcoin dollar rate (BTC/USDT) using deep and hybrid deep learning techniques", 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Pages 633-637, IEEE, October 2021.
• 7. Hamayel, M. J., and Owda, A. Y., "A novel cryptocurrency price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms", AI, Vol. 2, Issue. 4, Pages 477-496, 2021.
• 8. Yasar, H., and Kilimci, Z. H., "US dollar/Turkish lira exchange rate forecasting model based on deep learning methodologies and time series analysis", Symmetry, Vol. 12, Issue. 9, Pages 1553, 2020.
• 9. Yurtsever, M., "Gold price forecasting using LSTM, Bi-LSTM and GRU", European Journal of Science and Technology, Issue. 31, Pages 341-347, 2021.
• 10. Çoban, Ç., and Hayat, E., "Comparison of Different Deep Neural Network Models in Stock Market Analysis", Adnan Menderes University Journal of Social Sciences Institute, Vol. 10, Issue. 2, Pages 120-139, 2023.
• 11. Albayrak, E., and Saran, N., "Stock Price Prediction Using Statistical and Deep Learning Models", Turkish Informatics Foundation Journal of Computer Science and Engineering, Vol. 16, Issue. 2, Pages 161-169, 2023.
• 12. Kantar, O., and Kilimci, Z., "Deep learning based hybrid gold index (XAU/USD) direction forecast model", Journal of the Faculty of Engineering and Architecture of Gazi University, Vol. 38, Issue. 2, 2023.
• 13. Çelik, Y., "BIST100 Index Prediction Using Memory-Based LSTM and GRU Machine Learning Algorithms", Firat University Journal of Engineering Sciences, Vol. 36, Issue. 2, Pages 553-561, 2024.
• 14. Ayitey Junior, M., Appiahene, P., and Appiah, O., "Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis", Journal of Electrical Systems and Information Technology, Vol. 9, Issue. 1, Pages 14, 2022.
• 15. Nurhambali, M. R., Angraini, Y., and Fitrianto, A., "Implementation of Long Short-Term Memory for Gold Prices Forecasting", Malaysian Journal of Mathematical Sciences, Vol. 18, Issue. 2, 2024.
• 16. Qi, L., Khushi, M., and Poon, J., "Event-driven LSTM for forex price prediction", 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Pages 1-6, IEEE, December 2020.
• 17. Velicer, W. F., and Fava, J. L., "Time Series Analysis", In Weiner IB et al. editors, Handbook of Psychology, Pages 581-606, John Wiley & Sons, Hoboken, 2003.
• 18. Central Bank of the Republic of Turkey (CBRT), "Electronic Data Distribution System (EDDS)", https://evds2.tcmb.gov.tr, 2024.
• 19. DiPietro, R., and Hager, G. D., "Deep learning: RNNs and LSTM", In Brunette DM, Tengvall P, Textor M et al. editors, Handbook of Medical Image Computing and Computer Assisted Intervention, Pages 503-519, Academic Press, San Diego, 2020.
• 20. Xiao, J., and Zhou, Z., "Research progress of RNN language model", 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Pages 1285-1288, IEEE, June 2020.
• 21. Zhang, J., and Man, K. F., "Time series prediction using RNN in multi-dimension embedding phase space", SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics, Vol. 2, Pages 1868-1873, IEEE, October 1998.
• 22. Chai, T., and Draxler, R. R., "Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature", Geoscientific Model Development, Vol. 7, Issue 3, Pages 1247-1250, 2014.
• 23. Chicco, D., Warrens, M. J., and Jurman, G., "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation", Scientific Reports, Vol. 11, Issue 1, Pages 1-10, 2021.
• 24. Myttenaere, A., Golden, B., Le Grand, B., and Rossi, F., "Mean Absolute Percentage Error for regression models", Neurocomputing, Vol. 192, Pages 38-48, 2016.
• 25. Salim, M., & Djunaidy, A., Development of a cnn-lstm approach with images as time-series data representation for predicting gold prices. Procedia Computer Science, Vol. 234, Pages 333-340, 2024. 26. Zangana, H. M., & Obeyd, S. R., “Deep Learning-based Gold Price Prediction: A Novel Approach using Time Series Analysis”, Sistemasi: Jurnal Sistem Informasi, Vol. 13, Issue 6, Pages 2581-2591, 2024.
• 27. Marisetty, N., “Evaluating long-term and short-term relationships: Cointegration of NSE NIFTY with crude oil, gold, and USD/EUR currency pair. Gold, and USD/EUR Currency Pair”, Asian Journal of Management and Commerce, Vol 5. Issue 2, Pages 395–405, 2024.
• 28. Bohovic, D., “Comparison of LSTM and GRU Methods for Predicting Gold Exchange Rate against US Dollar”, International Journal Artificial Intelligent and Informatics, Vol. 3, Issue 1, Pages 24-29, 2025.
• 29. Islam, M. S., and Hossain, E., "Foreign exchange currency rate prediction using a GRU-LSTM hybrid network", Soft Computing Letters, Vol. 3, Pages 100009, 2021.
• 30. El Mahjouby, M., Bennani, M. T., Lamrini, M., El Far, M., Bossoufi, B., & Alghamdi, T. A., “Machine learning algorithms for forecasting and categorizing euro-to-dollar exchange rates”, IEEE Access, Vol. 3, Pages 74211-74217, 2024.
• 31. Buslim, N., Rahmatullah, I. L., Setyawan, B. A., and Alamsyah, A., "Comparing bitcoin's prediction model using GRU, RNN, and LSTM by hyperparameter optimization grid search and random search", 2021 9th International Conference on Cyber and IT Service Management (CITSM), Pages 1-6, IEEE, September 2021.
• 32. Cao, W., Zhu, W., Wang, W., Demazeau, Y., & Zhang, C., A deep coupled LSTM approach for USD/CNY exchange rate forecasting. IEEE Intelligent Systems, Vol. 35, Issue 1, Pages 43-53, 2020.
• 33. Ullah, U., Rehman, D., Khan, S., Rashid, H., & Ullah, I., Forecasting Foreign Exchange Rate with Machine Learning Techniques Chinese Yuan to US Dollar Using XGboost and LSTM Model. iRASD Journal of Economics, Vol. 6, Issue 3, Pages 761-775, 2024.