A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions. (December 2020)

• Record Type: Journal Article
• Authors: Patel, Mohil Maheshkumar; Tanwar, Sudeep; Gupta, Rajesh; Kumar, Neeraj
• Is Part Of: Journal of information security and applications. Volume 55(2020)
• Publication Date: 2020-12
• Subjects: Cryptocurrency -- Price prediction -- Litecoin -- Monero -- Recurrent Neural Network -- Long Short-Term Memory -- Gated Recurrent Unit -- Time Series; Computer security -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.jisa.2020.102583
• Languages: English
• ISSNs: 2214-2126

Abstract

Abstract: A cryptocurrency is a network-based digital exchange medium, where the records are secured using strong cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). It uses blockchain technology to make the transactions secure, transparent, traceable, and immutable. Due to these properties, the cryptocurrencies have gained popularity in almost all the sectors especially in financial sectors. Though, cryptocurrencies are getting recognition form the approval bodies, but still, the uncertainty and dynamism in their prices risk the investments substantially. Cryptocurrency price prediction has become a trending research topic globally. Many machine learning and deep learning algorithms such as Gated Recurrent Unit (GRU), Neural Networks (NN), and Long short-term memory (LSTM) have been used by the researchers to predict and analyze the factors affecting the cryptocurrency prices. In this paper, a LSTM and GRU-based hybrid cryptocurrency prediction scheme is proposed, which focuses on only two cryptocurrencies, namely Litecoin and Monero. The results depict that the proposed scheme accurately predicts the prices with high accuracy, revealing that the scheme can be applicable in various cryptocurrencies price predictions.