Comparison of CNN-LSTM Hybrid and CNN Methods for Ethereum (ETH) to US Dollar (USD) Exchange Rate Prediction
(1) * Daniel Regine Mail (College of Micronesia-FSM, Micronesia, Federated States of)
(2) Anatoly Zabarnyi Mail (Lviv Polytechnic National University, Ukraine)
*corresponding author

Abstract


This research compares the effectiveness of the hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) method and the Convolutional Neural Network (CNN) method in predicting the Ethereum (ETH) exchange rate against the United States Dollar (USD). The research uses historical ETH/USD data from Yahoo Finance for the period 2017-2022. Evaluation of the two models was carried out using the performance metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and accuracy rate. The results showed that the CNN-LSTM hybrid model significantly outperformed the CNN model in predicting the ETH/USD exchange rate with a Test RMSE value of 94.67 compared to 129.02 for CNN, as well as an accuracy rate of 96.31% versus 94.89%. These findings contribute to the fintech literature by providing empirical evidence of the superiority of hybrid methods for high volatility cryptocurrency exchange rate prediction.

   

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https://doi.org/10.33292/ijarlit.v3i1.42 		      

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