Prediction of Euro to US Dollar Exchange Rate Using CNN Method with Grid Optimization
(1) * Faqihuddin Al Anshori Mail (Universitas PGRI Yogyakarta, Indonesia)
(2) S Pidgeon Mail (University of Technology Sydney, Australia)
*corresponding author

Abstract


This research compares the performance of Convolutional Neural Network (CNN) models without optimization and CNN with Grid Search optimization in predicting the Euro exchange rate against the United States Dollar. Data obtained from Yahoo Finance for the period 2018-2023. The results showed that the CNN model with Grid Search optimization provided better performance with an RMSE value of 0.01, MAE of 0.01, MAPE of 0.61%, and R² of 0.8586 on test data, and prediction accuracy reached 99.39%. Grid Search optimization successfully found the best parameters with batch_size 32, dense_units 50, filters 64, kernel_size 3, and learning_rate 0.001. This research proves that hyperparameter optimization can improve the performance of CNN models in predicting currency exchange rates, which can be a decision support tool for foreign exchange market players.

Keywords


CNN; exchange rate prediction; Euro; US Dollar; Grid Search optimization deep

   

DOI

https://doi.org/10.33292/ijarlit.v3i2.45 		      

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10.33292/ijarlit.v3i2.45 Abstract views : 108 | PDF views : 36

   

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