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Cryptocurrency Forecasting Using Deep Learning Models: A Comparative Analysis

Rachid Bourday, Issam Aatouchi, Mounir Ait Kerroum, Ali Zaaouat

Abstract


Bitcoin has recently grown to prominence as a decentralized digital currency, attracting significant interest for its potential transformation of the financial market. Forecasting Bitcoin's price is crucial for investors, traders, and academics, given the currency's inherent volatility, which makes accurately predicting future prices challenging. This article aims to provide a comprehensive and comparative analysis of Deep Learning Forecasting Models in order to predict Bitcoin prices in the short and medium terms: Transformer with XGBoost, Transformer with ANN, Transformer with LSTM, and Transformer with SVR. This study is the first to explore the effectiveness of transformer-based architectures, particularly focusing on feature extraction, in complex financial market predictions. Therefore, we trained these models using historical Bitcoin data from 2016 to 2023 and evaluated their performance on a test dataset. Our experiments demonstrate that the Transformer with the XGBoost model outperforms the baseline models, achieving a Mean Absolute Error (MAE) of 0.011 and a Root Mean Squared Error (RMSE) of 0.018. Our findings suggest that the use of advanced deep learning techniques effectively manages the complexities of the cryptocurrency market, offering significant improvements over traditional methods and guiding investors in the cryptocurrency markets.

 

Doi: 10.28991/HIJ-2024-05-04-013

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Keywords


Forecasting; Deep Learning; Machine learning; Transformer; LSTM; XGBoost.

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DOI: 10.28991/HIJ-2024-05-04-013

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