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

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

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)138633-138646
Number of pages14
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • Cryptocurrency
  • direction algorithm
  • Gated Recurrent Unit
  • inter-dependencies
  • Litecoin
  • Long Short-Term Memory
  • parent coin's direction
  • price prediction
  • Zcash

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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