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Applications of Some Deep Learning Algorithms to Predict Trend in the Forex Exchange Market | ||
| Journal of Mathematics and Modeling in Finance | ||
| دوره 5، شماره 2، دی 2025، صفحه 65-75 اصل مقاله (423.38 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2025.85949.1183 | ||
| نویسندگان | ||
| Mohammad Ali Jafari* ؛ Sina Ghasemilo | ||
| Financial Mathematics Department, Finance Faculty, Kharazmi University | ||
| چکیده | ||
| Predicting time series has always been one of the challenges in the financial markets. With the increase in the amount of data, the need to use modern tools instead of classical statistical and time series methods has become clear. In this paper, some deep learning algorithms such as Multilayer Perceptrons (MLPs), Keras Classification, Temporal Fusion Transformer (TFT, developed by Google), Extreme Learning Machine Classification (ELMC) and Propagation Hierarical Learning Network (PHILNet) are used for trading on the foreign exchange market. The efficiency and accuracy of these algorithms are presented. In this order, the EUR/USD data is used as input for the above algorithms. | ||
| کلیدواژهها | ||
| Deep learning؛ Forex market؛ Trend of the EUR/USD | ||
| مراجع | ||
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[1] Das, A., Kumar, K., and Sahu, K. K., Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis, Expert Syst. Appl. 92 (2018), 103–116. [2] Sahu, K. K., Nayak, S. C., and Behera, H. S., Forecasting exchange rates using Extreme Learning Machines enhanced with metaheuristic algorithms, Internat. J. Comput. Intell. Syst. 16 (2023), no. 1, 67–85. [3] Jim´enez Navarro, M. J., Mart´ınez Ballesteros, M. d. M., Mart´ınez Alvarez, F., and Asencio ´ Cort´es, G., PHILNet: A novel efficient approach for time series forecasting using deep learning, Inform. Sci. 632 (2023), 815–832. [4] Zhao, L. and Yan, W. Q., Prediction of currency exchange rate based on Transformers, J. Risk Financ. Manag. 17 (2024), no. 8, 332. [5] Khan, I. A., Evaluating foreign exchange rate forecasting: A deep dive into LSTM, MLP, and Random Forest approaches, Preprint (2023). [6] Nguyen, Q. A., Son, H., and Nguyen, P., A lightweight multi-head attention transformer for stock price forecasting, Preprint (2023). [7] Weng, F., Zhang, H., and Yang, C., Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic, Resour. Policy (2021), 102148. [8] Weng, F., Chen, Y., Wang, Z., Hou, M., Luo, J., and Tian, Z., Gold price forecasting research based on an improved online extreme learning machine algorithm, J. Ambient Intell. Humaniz. Comput. 11 (2020), 4101–4111. [9] Laborda, J., Ruano, S., and Zamanillo, I., Multi-country and multi-horizon GDP forecasting using Temporal Fusion Transformers, Math. 11 (2023), no. 12, 2625. [10] Ouyang, R., Pei, T., Fang, Y., and Zhao, Y., Commodity systemic risk and macroeconomic predictions, Energy Econ. (2024), 107807. [11] Lim, B., Arik, S., Loeff, N., and Pfister, T., Temporal Fusion Transformers for interpretable multi-horizon time series forecasting, Internat. J. Forecast. 37 (2021), no. 4, 1748–1764. | ||
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