Applications of Some Deep Learning Algorithms to Predict Trend in the Forex Exchange Market

[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.