Muhammad Fauzi Fayyad, Viki Kurniawan, Muhammad Ridho Anugrah, Baıhaqı Hılmı Estanto, Tasnim Bilal
{"title":"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","authors":"Muhammad Fauzi Fayyad, Viki Kurniawan, Muhammad Ridho Anugrah, Baıhaqı Hılmı Estanto, Tasnim Bilal","doi":"10.57152/predatecs.v2i1.1094","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":516904,"journal":{"name":"Public Research Journal of Engineering, Data Technology and Computer Science","volume":"118 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"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\",\"authors\":\"Muhammad Fauzi Fayyad, Viki Kurniawan, Muhammad Ridho Anugrah, Baıhaqı Hılmı Estanto, Tasnim Bilal\",\"doi\":\"10.57152/predatecs.v2i1.1094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":516904,\"journal\":{\"name\":\"Public Research Journal of Engineering, Data Technology and Computer Science\",\"volume\":\"118 4\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Public Research Journal of Engineering, Data Technology and Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.57152/predatecs.v2i1.1094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Public Research Journal of Engineering, Data Technology and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.57152/predatecs.v2i1.1094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.