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Article A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms Mohammad J. Hamayel and Amani Yousef Owda * Department of Natural, Engineering and Technology Sciences, Arab American University, Ramallah P600, Palestine; m.abdjoudeh@student.aaup.edu * Correspondence: Amani.Owda@aaup.edu Abstract: Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume. Keywords: cryptography; blockchain; cryptocurrency; artificial intelligence (AI); machine learning 1. Introduction Cryptocurrency is a virtual or digital currency used in financial systems [1,2]. It is secured by cryptography that makes it impossible to be counterfeited or double-spent. Furthermore, it is not issued from a central authority or central banks, and it is decentralized virtual currencies that can be converted via cryptographic procedures [3] and this make it distinguishable from traditional currencies. The other feature is that it is created by technology called blockchain [4], which is an extremely complex, and aims to storing data that makes it difficult or impossible to alter, hack, or defraud the system. Bitcoin has begun to carve out a niche for itself, which may either help cryptocurrencies to gain widespread acceptance or be the major cause of their demise. Cryptocurrencies are still in their infancy, and it is difficult to predict whether they will ever be widely used in global markets or not [5]. The most prominent cryptocurrency, Bitcoin, was established in 2009 and for more than two years was the sole Blockchain-based cryptocurrency. Today, however, there are over 5000 cryptocurrencies and 5.8 million active users in the cryptocurrency industry [6]. Because of its intrinsic nature of mixing encryption technology with monetary units, Bitcoin has recently gotten a lot of attention in the disciplines of economics, cryptography, and computer science [7]. Blockchain (BC), the technology that underpins the Bitcoin cryptocurrency system, is widely seen as critical in providing the backbone for assuring greater security and privacy Citation: Hamayel,M.J.;Owda,A.Y. A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms. AI 2021, 2, 477–496. https://doi.org/10.3390/ai2040030 Academic Editor: Amir Mosavi Received: 8 September 2021 Accepted: 5 October 2021 Published: 13 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). AI 2021, 2, 477–496. https://doi.org/10.3390/ai2040030 https://www.mdpi.com/journal/aiPDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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