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AI 2021, 2 480 Bitcoin closing prices for the creation of Bitcoin prediction models. The authors of [31] used machine learning techniques to address both a multiple regression technique that relies on highly correlated characteristics and a deep learning mechanism that uses a conjugate gradient mechanism in conjunction with a linear search for BTC price prediction. In [32], the price movements of Bitcoin, Ethereum, and Ripple are analyzed. The authors utilize powerful artificial intelligence frameworks, including a fully linked artificial neural network (ANN) and a long short-term memory (LSTM) recurrent neural network, and they discovered that ANN relies more on long-term history, whereas LSTM relies more on short- term dynamics, implying that LSTM is more efficient at extracting meaningful information from historical memory than ANN. The study in [33] on Bitcoin daily price prediction with high-dimensional data reveals that logistic regression and linear discriminant analysis achieve an accuracy of 66%. On the other hand, surpassing (a sophisticated machine learning algorithm) outperforms the benchmark results for daily price prediction, with statistical techniques and machine learning algorithms having the greatest accuracies of 66% and 65.3%, respectively. The study in [34] examines the use of neural networks (NN), support vector machines (SVM), and random forest (RF). The findings demonstrate that machine learning and sentiment analysis may be used to anticipate cryptocurrency markets (with Twitter data alone being able to predict specific coins) and that NN outperforms the other models. In [35], the LSTM model is used to predict and find methods for forecasting Bitcoin on the stock market through Yahoo Finance that may predict a result of more than 12,600 USD in the days after the prediction. Due to the importance of the development of a robust and reliable method for predicting cryptocurrency prices, researchers have focused on more innovative models. In [36], both linear and non-linear time-series components of the stock dataset were used for forecasting using the hybrid model. In the non-linear time series forecast, CNN and Seq2Seq LSTMs were successfully coupled for dynamic modeling of short- and long-term dependent patterns. The study in [37] focused on social factors, which are increasingly being utilized for online transactions throughout the world, by using a multi-linear regression model and that analyzes two big capital market cryptocurrencies, BTC and LTC. The authors of [37] found that the R2 scores were 44% for LTC and 59% for BTC. Ref. [38] used two different LSTM models (a standard LSTM model and an LSTM with an AR(2) model). This study presented a forecasting framework, using an LSTM model to forecast Bitcoin daily prices. The study in [38] found that the model with AR(2) was better than LSTM with an RMSE of 247.33. Researchers in [39] compared three different models (ARIMA, LSTM and GRU) for predicting BTC’s price. The experimental outcomes in [39] showed that ARIMA achieved the best performance with a MAPE of 2.76% and RMSE of 302.53. The study in [40] presented two types of prediction models constructed using Bayesian optimized RNN and LSTM to predict the price of BTC. The study revealed that LSTM showed better performance and achieved an accuracy of 52% and RMSE of 8%. The investment process mainly depends on the historical price of a cryptocurrency. One of the most important strategies that the investor depends on is building Markov chains. This strategy consists of multiple decision trees that are used to identify the cryptocurrency that is estimated to provide a greater return when sold and then comparing the estimation with the actual figure [41]. 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. 3. Materials and Methods To achieve the aims of this paper, we trained three distinct models for three different forms of cryptocurrency price prediction using historical cryptocurrency prices. Then, in order to evaluate the suggested schemes’ performances, we compare the accuracy of our proposed model to that of current models by following five stages: (1) collecting historicalPDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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