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.
Highlights
Due to the importance of prediction in the investment process that many people depend on to earn revenue, this paper focuses on three models that can predict future cryptocurrency prices using machine learning algorithms and artificial intelligence approaches to achieve accurate prediction models with the aim of helping investors
We present and compare three types of algorithms—long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM—to prememory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM—to predict the price of three types of cryptocurrency based on historical data—Bitcoin (BTC), dict the price of three types of cryptocurrency based on historical data—Bitcoin (BTC), Litecoin (LTC) and Ethereum (ETH)
They show that the difference between the predicted and the actual price is very small with a mean absolute percentage error of 0.8474%, and a root mean square error of 3.069
Summary
It is secured by cryptography that makes it impossible to be counterfeited or double-spent. 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. The most prominent cryptocurrency, Bitcoin, was established in 2009 and for more than two years was the sole Blockchain-based cryptocurrency. There are over 5000 cryptocurrencies and 5.8 million active users in the cryptocurrency industry [6]
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