Comparison of LSTM and GRU Models for Forex Prediction

Authors

  • Mohammad Rezza Pahlevi Universitas Amikom Yogyakarta
  • Kusrini Universitas Amikom Yogyakarta
  • Tonny Hidayat Universitas Amikom Yogyakarta

DOI:

10.33395/sinkron.v8i4.12709

Keywords:

Forex, Prediction, LSTM, GRU, RMSE, MAPE

Abstract

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%

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Author Biographies

Mohammad Rezza Pahlevi, Universitas Amikom Yogyakarta

 

 

Kusrini, Universitas Amikom Yogyakarta

 

 

Tonny Hidayat, Universitas Amikom Yogyakarta

 

 

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How to Cite

Pahlevi, M. R., Kusrini, K., & Hidayat, T. . (2023). Comparison of LSTM and GRU Models for Forex Prediction. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2254-2263. https://doi.org/10.33395/sinkron.v8i4.12709

Issue

Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023

Section

Articles

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Copyright (c) 2023 Mohammad Rezza Pahlevi, Kusrini, Tonny Hidayat

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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