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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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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.

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Availability of Data and Materials

The data was collected from https://finance.yahoo.com, saved as a CSV file, and remained with the authors. Therefore, the data are available upon request.

Abbreviations

BTC-USD:

Bitcoin-adjusted close price (US dollars)

CNN:

Convolution neural networks

ETH-USD:

Ethereum-adjusted close price (US dollars)

ReLU:

Rectified linear unit

LSTM:

Long short-term memory

RMSE:

Root mean square error

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Authors and Affiliations

  1. Department of Statistics, University of Dar es Salaam, Dar es Salaam, Tanzania

    Godfrey Joseph Saqware

  2. Department of Statistics, Yenepoya (Deemed to Be University), Mangalore, India

    Ismail B

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  1. Godfrey Joseph Saqware
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  2. Ismail B
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Correspondence to Godfrey Joseph Saqware.

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Saqware, G.J., B, I. Hybrid Deep Learning Model Integrating Attention Mechanism for the Accurate Prediction and Forecasting of the Cryptocurrency Market. Oper. Res. Forum 5, 19 (2024). https://doi.org/10.1007/s43069-024-00302-2

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