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AI 2021, 2 479 or negatively affect other cryptocurrencies. The authors of [24] reveal that gold as an independent currency can be used as a good hedging instrument to decrease the risk related to unexpected movement in the cryptocurrency market. Cryptocurrency prices are difficult to forecast due to price volatility and dynamism. Around the world there are hundreds of cryptocurrencies that clients use. In this paper, we focus on three of the most popular ones. As a result, the paper aims to achieve the following by using deep leaning algorithms, which can discover hidden patterns from data, integrate them, and create far more efficient predictions: • Presenting a comprehensive study of the various existing schemes to predict the prices of BTC, ETH, and LTC cryptocurrencies. • Using AI algorithms such as LSTM, bi-LSTM, and GRU to accurately predict the prices of cryptocurrencies. • Utilizing long short-term memory (LSTM), a deep learning algorithm, and Fbprophet, which is an auto machine learning algorithm, for prediction. • Evaluating the proposed hybrid models using evaluation matrices such as RMSE and MAPE for Bitcoin, Ethereum, and Litecoin. The main idea behind these models is to achieve a reliable prediction model that investors can rely on based on historical cryptocurrency prices. Moreover, the paper aims to answer the following research questions: ‘How can machine learning algorithms help investors and decision makers to predict cryptocurrency prices?’ and ‘What is the best model for predicting future cryptocurrency prices?’ This section provides an overview of cryptocurrencies and the remainder of the paper is structured as follows: Section 2 describes the literature review and previous work in this field, Section 3 presents the modeling results and the statistical analysis of the data, Section 4 describes the used dataset, Section 5 illustrates the experimental results, Section 6 presents a comparison between the model proposed in this paper and those of other studies in the literature, and Section 7 summarizes the overall conclusions of the paper. 2. Literature Review Machine learning (ML) is a type of artificial intelligence that can predict the future based on past data. ML-based models have various advantages over other forecasting models as prior research has shown that it not only delivers a result that is nearly or exactly the same as the actual result, but it also improves the accuracy of the result [25]. Examples of machine learning include neural networks (NN), support vector machines (SVM), and deep learning. The authors of [26] demonstrate that incorporating cryptocurrency into a portfolio improves its effectiveness in two ways. The first is to reduce the standard deviation, and the second is to provide investors with more allocation options. The best cryptocurrency allocation was reported to be in the range from 5% to 20%, depending on the risk tolerance of the investor. The authors of [27] focus on time series data forecasting in particular and apply two machine learning algorithms, random forests (RF) and stochastic gradient boosting machine (SGBM). The results show that the ML ensemble technique can be used to anticipate Bitcoin values. The decision-making process needs to make the appropriate decision at the right time, reducing the risks associated with the investment process. In [28], a hybrid cryptocurrency prediction system based on LSTM and GRU is presented, focusing on two cryptocurrencies, Litecoin and Monero. The authors of [29] use minute-sampled Bitcoin returns over 3 h periods to aggregate RV data. A variety of machine learning methods, including ANN (MLP, GRU, and LSTM), SVM, and ridge regression, were used to predict future values based on past samples, which are compared to the heterogeneous auto-regressive realized volatility (HARRV) model with optimized lag parameters. The findings show that the suggested system correctly predicts prices with high accuracy, indicating that the method may be used to forecast prices for a variety of cryptocurrencies. The authors of [30] employ the traditional support vector machine and linear regression methods to forecast Bitcoin values. This research takes into account a time series prediction made up of everydayPDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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