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A Data-driven Deep Learning Approach for Bitcoin Price Forecasting

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  • Parth Daxesh Modi
  • Kamyar Arshi
  • Pertami J. Kunz
  • Abdelhak M. Zoubir

Abstract

Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality data to leverage their power. There are some techniques such as augmentation that can help us with increasing the dataset size, but we cannot exploit them on historical bitcoin data. As a result, we propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.

Suggested Citation

  • Parth Daxesh Modi & Kamyar Arshi & Pertami J. Kunz & Abdelhak M. Zoubir, 2023. "A Data-driven Deep Learning Approach for Bitcoin Price Forecasting," Papers 2311.06280, arXiv.org.
    • Handle: RePEc:arx:papers:2311.06280
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    File URL: http://arxiv.org/pdf/2311.06280
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    References listed on IDEAS

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    1. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.
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