循环神经网络双长短时记忆、门控循环单元和双门控循环单元在预测印尼盾兑美元汇率中的应用

Muhammad Fauzi Fayyad, Viki Kurniawan, Muhammad Ridho Anugrah, Baıhaqı Hılmı Estanto, Tasnim Bilal
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摘要

外汇汇率对一个国家的经济发展起着至关重要的作用,影响着长期的投资决策。本研究旨在利用递归神经网络(RNN)架构的深度学习模型,尤其是双长短期记忆(Bi-LSTM)、门控递归单元(GRU)和双门控递归单元(Bi-GRU),预测印尼盾兑美元(USD)的汇率。数据集采用了从雅虎财经获取的 2013 年 1 月 1 日至 2023 年 11 月 3 日的每日汇率历史数据。模型的训练和评估过程是根据优化器、批量大小和时间步长等不同参数进行的。通过最小化均方误差 (MSE)、均方根误差 (RMSE)、平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE) 来确定最佳模型。在测试的模型中,采用 Nadam 优化器、批量大小为 16、时间步长为 30 的 GRU 模型表现最佳,MSE 为 3741.6999,RMSE 为 61.1694,MAE 为 45.6246,MAPE 为 0.3054%。预测结果表明,在未来 30 天内,印尼盾对美元的汇率将呈走强趋势,这有可能成为投资决策的考虑因素,并显示出印尼经济增长的美好前景。
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Application of Recurrent Neural Network Bi-Long Short-Term Memory, Gated Recurrent Unit and Bi-Gated Recurrent Unit for Forecasting Rupiah Against Dollar (USD) Exchange Rate
Foreign exchange rates have a crucial role in a country's economic development, influencing long-term investment decisions. This research aims to forecast the exchange rate of Rupiah to the United States Dollar (USD) by using deep learning models of Recurrent Neural Network (RNN) architecture, especially Bi-Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-Gated Recurrent Unit (Bi-GRU). Historical daily exchange rate data from January 1, 2013 to November 3, 2023, obtained from Yahoo Finance, was used as the dataset. The model training and evaluation process was performed based on various parameters such as optimizer, batch size, and time step. The best model was identified by minimizing the Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Among the models tested, the GRU model with Nadam optimizer, batch size 16, and timestep 30 showed the best performance, with MSE 3741.6999, RMSE 61.1694, MAE 45.6246, and MAPE 0.3054%. The forecast results indicate a strengthening trend of the Rupiah exchange rate against the USD in the next 30 days, which has the potential to be taken into consideration in making investment decisions and shows promising economic growth prospects for Indonesia.
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