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Predicting Bitcoin and Ethereum Prices Using the Long Short- Term Memory (LSTM) Model

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The LSTM model achieved high prediction accuracy for Bitcoin and Ethereum, with RMSE values of 0.0137 and 0.0152 respectively.
Using a 90:10 split of combined data from Yahoo Finance and Binance substantially improved prediction performance.
Data preprocessing steps included Min-Max normalization and creating 24-day sliding input windows to enhance model training.
Development of a web-based application allows real-time predictions, making these results accessible for users and investors.

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This analysis employs LSTM models to forecast prices of Bitcoin and Ethereum, suggesting data integration enhances accuracy.

Aswadi et al. (2025) studied this question.

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