Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

【Author】 Tanwar, Sudeep; Patel, Nisarg P.; Patel, Smit N.; Patel, Jil R.; Sharma, Gulshan; Davidson, Innocent E.

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】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 cannot 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.

【Keywords】Cryptography; Bitcoin; Predictive models; Government; Forecasting; Blockchains; Currencies; Cryptocurrency; price prediction; Litecoin; Zcash; Long Short-Term Memory; Gated Recurrent Unit; inter-dependencies; direction algorithm; parent coin's direction

【发表时间】2021

【收录时间】2022-01-02

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