Comparison of LSTM and GRU Models for Forex Prediction
DOI:
10.33395/sinkron.v8i4.12709Keywords:
Forex, Prediction, LSTM, GRU, RMSE, MAPEAbstract
Trading foreign currencies worth trillions of dollars takes place daily in the forex market, characterized by highly volatile movements. The forex market operates on bid and ask prices, with exchange rates determined by the principles of supply and demand. Trading involves currency pairs like EUR/USD, where the value of the Euro is compared to the US Dollar, serving as a basis for analyzing price fluctuations. Due to the volatile nature of forex, market participants must make informed decisions when buying and selling, as improper choices can result in financial losses. One approach to mitigating risk in forex trading decisions is through the use of forecasting techniques. This research study employs LSTM and GRU methods to predict forex trends, which are evaluated using various dataset divisions. The most accurate results are obtained using a dataset of 4979, split into three equal parts: 80% for training, 10% for validation, and 10% for testing. This approach yields an RMSE value of 0.054, MAPE of 0.037, and R-square of 97%
Downloads
References
Abedin, M. Z., Moon, M. H., Hassan, M. K., & Hajek, P. (2021). Deep learning-based exchange rate prediction during the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04420-6
Cabrera, F. (2019). Application of ARIMA, RNN and LSTM on Foreign Exchange Rate Prediction.
Dhanardono Bhima, & Atmodjo Danang. (2022). Pemodelan Tiga Dimensi Menggunakan Total Station, Terrestrial Laser Scanner Dan Unmanned Aerial Vehicle. Deepublish,.
Fandango, Armando., Idris, Ivan., & Navlani, Avinash. (2021). Python Data Analysis - Third Edition. Packt Publishing.
Henríquez, J., & Kristjanpoller, W. (2019). A combined Independent Component Analysis–Neural Network model for forecasting exchange rate variation. Applied Soft Computing Journal, 83. https://doi.org/10.1016/j.asoc.2019.105654
Hu, Z., Liu, W., Bian, J., Liu, X., & Liu, T. Y. (2018). Listening to chaotic whispers: A deep learning framework for news-oriented Stock trend prediction. WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 2018-Febuary, 261–269. https://doi.org/10.1145/3159652.3159690
Islam, M. S., & Hossain, E. (2021). Foreign exchange currency rate prediction using a GRU-LSTM hybrid network. Soft Computing Letters, 3, 100009. https://doi.org/10.1016/j.socl.2020.100009
Khrisna Wardhani Anindya, & Israwan Fajar. (2022). Buku di Google Play Teknik Peramalan Pada Teknologi Informasi. Get Press.
Mahan Zaky, A. (2022). Forecasting Jumlah Penggunaan Obat Digestive Enzymes dengan Algoritma GRU. Journal of Informatics and Vocational Education, 5(2), 48–55. https://jurnal.uns.ac.id/joive/index
Muflikhah, L., & Cholissodin, I. (2022). Peramalan Kasus Positif COVID-19 di Jawa Timur menggunakan Metode Hybrid ARIMA-LSTM (Vol. 6, Issue 9). http://j-ptiik.ub.ac.id
Primananda, S. B., & Isa, S. M. (2021). Forecasting Gold Price in Rupiah using Multivariate Analysis with LSTM and GRU Neural Networks. Advances in Science, Technology and Engineering Systems Journal, 6(2), 245–253. https://doi.org/10.25046/aj060227
Puspita, H. (2022). Pengantar Teknologi Informasi .
Qi, L., Khushi, M., & Poon, J. (2020, December 16). Event-Driven LSTM for Forex Price Prediction. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020. https://doi.org/10.1109/CSDE50874.2020.9411540
Sarangi, P. K., Chawla, M., Ghosh, P., Singh, S., & Singh, P. K. (2020). FOREX trend analysis using machine learning techniques: INR vs USD currency exchange rate using ANN-GA hybrid approach. Materials Today: Proceedings, 49, 3170–3176. https://doi.org/10.1016/j.matpr.2020.10.960
Satyo Bayangkari Karno, A., Noer Ali, J. K., & Bekasi, K. (2020). Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory). Journal of Information and Information Security (JIFORTY), 1(1), 1–8. http://ejurnal.ubharajaya.ac.id/index.php/jiforty
Ulina, M., Purba, R., & Halim, A. (2020, November 3). Foreign Exchange Prediction using CEEMDAN and Improved FA-LSTM. 2020 5th International Conference on Informatics and Computing, ICIC 2020. https://doi.org/10.1109/ICIC50835.2020.9288615
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2023 Mohammad Rezza Pahlevi, Kusrini, Tonny Hidayat
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.