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Hybrid Deep Learning Model Integrating Attention Mechanism for the Accurate Prediction and Forecasting of the Cryptocurrency Market

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  • Godfrey Joseph Saqware

    (University of Dar es Salaam)

  • Ismail B

    (Yenepoya (Deemed to Be University))

Abstract

Currently, cryptocurrency has become one of the most traded worldwide financial instruments. The nature of cryptocurrency is complex and is also deemed a perplexing finance problem. This study applied deep learning methods to predict and forecast the Bitcoin (BTC-USD) and Ethereum (ETH-USD) cryptocurrency market-adjusted close prices. Based on root mean square error (RMSE), the hybrid CNN-LSTM model with Attention Mechanism outperformed CNN and LSTM models in predicting the ETH-USD-adjusted close price. In addition, the traditional LSTM model predicted well the BTC-USD-adjusted close price. In forecasting, the hybrid CNN-LSTM model produced better results for both BTC-USD- and ETH-USD-adjusted close prices compared to individual models. Furthermore, the hybrid model performed well at shorter forecasting horizon and loses its forecasting ability when the horizon is long. The result plays a significant role in analyzing the future cryptocurrency market. The traders and financial analysts can easily understand the future market trend using the hybrid model. Thus, this may help traders to easily trade in the complex and challenging cryptocurrency markets.

Suggested Citation

  • Godfrey Joseph Saqware & Ismail B, 2024. "Hybrid Deep Learning Model Integrating Attention Mechanism for the Accurate Prediction and Forecasting of the Cryptocurrency Market," SN Operations Research Forum, Springer, vol. 5(1), pages 1-19, March.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:1:d:10.1007_s43069-024-00302-2
    DOI: 10.1007/s43069-024-00302-2
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    References listed on IDEAS

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