Forecasting the EUR/USD Exchange Rate Using ARIMA and Machine Learning Models
DOI:
https://doi.org/10.56294/dm2024.368Keywords:
propose Autoregressive Integrated Moving Average, Long Short-Term Memory, Recurrent neural network, forecasting dataAbstract
The present paper compared ARIMA with two machine learning algorithms, for forecasting USD/EUR exchange rate data. The experimental results indicated that the performance of ARIMA fell between that of recurrent neural networks and long short-term memory machine learning algorithms.
References
1. Peter JB, Richard AD. Introduction to Time Series and Forecasting. 2nd ed. Colorado: springer; 2002.
2. William WSW. Time series analysis, Univariante and multivariante methods. 2nd ed. New York: Addison-Wesley; 2006.
3. Georgee P, Gwilymm J, Gregorec R, Gretam L. Time Series Analysis Forecasting and Control. 4th ed. New Jersey: John Wiley & Sons; 2016.
4. Regina K, Agustın M. Notes on time series analysis ARIMA models and signal extraction. Banco de Espana, Servicio de Estudios Documento de Trabajo. 2023; 0012: 1-73.
5. Joseph LM, Kevin EB, Gemunu HG. Integration I(d) of Nonstationary Time Series Stationary and nonstationary increments. Texas: Center for Superconductivity University of Houston; 2008.
6. Ali S, Majid K. A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets. Expert Systems With Applications. 2022;208(1):118-124.
7. Alireza S, Amir D, Mahdi MZ. Combined ensemble multi-class SVM and fuzzy NSGA-II for trend forecasting and trading in Forex markets. Expert Systems With Applications. 2021;185(10).
8. Jonatha SP, Raydonal O, Anderson A. A novel fusion Support Vector Machine integrating weak and sphere models for classification challenges with massive data. Decision Analytics Journal. 2024;11.
9. Chen L, Jiong RW. Jing Wan, Osman Taylan, Cheng-Wei Fei. Adaptive directed support vector machine method for the reliability evaluation of aeroengine structure. Reliability Engineering and System Safety. 2024;246.
10. Xinwei C, Adam F, Xujin P, Zenan Z, Vasilios K, Predrag S, Ivona B, Shuai L. A novel recurrent neural network based on line portfolio analysis for high frequency trading. Expert Systems With Applications. 2021;185.
11. Andrea C, Claudio G. Residual Echo State Networks: residual recurrent neural networks with stable dynamics and fast learning. Neurocomputing. 2024.
12. Mohammed SA, Yi MR, Feiyang O, Fahim A, Panagiotis DC. Model predictive control of non linear processesusing transfer learning-based recurrent neural networks. Chemical Engineering Research and Design. 2024;205.
13. Chenjie S, Massimo DP. Improving trading technical analysis with TensorFlow Long Short Term Memory (LSTM) Neural Network. The Journal of Finance and Data Science. 2019; 5(1) :1-11.
14. Qian K, Dengxiu Y, Kang HC, Zhen W. Deterministic convergence analysis for regularized long short-term memory and its application to regression and multi-classification problems. Engineering Applications of Artificial Intelligence. 2024; 133(E).
15. Yildirim K, Sheldon BG, Hossein E, Dorcas SE, Farshad B, Yavuz CK, Aman A. Improving the accuracy of short-term multiphase production forecasts in unconventional tight oil reservoirs using contextual Bi-directional long short-term memory. Geoenergy Science and Engineering. 2024; 235.
16. Islam MS, Hossain E. Foreign exchange currency rate prediction using a GRU-LSTM hybrid network. Soft Computing Letters. 2021; 3.
17. David A, Belen SM, Antonio P. Hybrid ARMA-GARCH-Neural Networks for intraday strategy exploration in high-frequency trading. Pattern Recognition. 2024; 148.
18. Jaimin S, Darsh V, Manan S. A comprehensive review on multiple hybrid deep learning approaches for stock prediction. Soft Computing Letters. 2022; 16.
19. Karolina M, Agata K, Aleksandra M, Kateryna C, Klaudia K, Paweł G, Marcin H, Krzysztof L, Adrianna K, Marcin P, Artur R, Mykola D. Financial Time Series Forecasting: Comparison of Traditional and Spiking Neural Networks. 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. 2021; 192: 5023-5029.
20. Divish, Mina J , Benjamin R, Xin C, Grazziela PF. Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management. Sensors. 2020; 20(723) :1-23.
Published
Issue
Section
License
Copyright (c) 2024 Said LAKHAL (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
