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Title: Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations
Authors: Tanwar, Sudeep 
Patel, Nisarg P. 
Patel, Smit N. 
Patel, Jil R. 
Sharma, Gulshan
Davidson, Innocent Ewaen 
Keywords: Cryptocurrency;Price prediction;Litecoin, Zcash;Long Short-Term Memory;Gated Recurrent Unit;Inter-dependencies;Direction algorithm;Parent coin’s direction
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Source: Tanwar, S., Patel, N.P., Patel, S.N., Patel, J.R., Sharma, G., Davidson, I.E. 2021. Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations. IEEE Access. : 1-1. doi:10.1109/access.2021.3117848
Journal: IEEE Access 
Blockchain technology is becoming increasingly popular because of its applications in
various fields. It gives an edge over the traditional centralized methods as it provides decentralization,
immutability, integrity, and anonymity. The most popular application of this technology is cryptocurrencies,
which showed a massive rise in their popularity and market capitalization in recent years. Individual
investors, big institutions, and corporate firms are investing heavily in it. However, the crypto market is
less stable than traditional commodity markets. It can be affected by many technical, sentimental, and legal
factors, so it is highly volatile, uncertain, and unpredictable. Plenty of research has been done on various
cryptocurrencies to forecast accurate prices, but the majority of these approaches can not be applied in
real-time. Motivated from the aforementioned discussion, in this paper, we propose a deep-learning-based
hybrid model (includes Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM)) to predict
the price of Litecoin and Zcash with inter-dependency of the parent coin. The proposed model can be used
in real-time scenarios and it is well trained and evaluated using standard data sets. Results illustrate that the
proposed model forecasts the prices with high accuracy compared to existing models
ISSN: 2169-3536 (Online)
DOI: 10.1109/access.2021.3117848
Appears in Collections:Research Publications (Engineering and Built Environment)

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