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AI 2021, 2 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 481 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 historical cryptocurrency data; (2) data exploration and visualization; (3) training three types of cryptocurrency data; (2) data exploration and visualization; (3) training three types of models; (4) testing the models; and (5) extracting and comparing the results. models; (4) testing the models; and (5) extracting and comparing the results. In this section, we present and compare three types of algorithms—long short-term In this section, we present and compare three types of algorithms—long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (bi-LSTM)—to pre- memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (bi-LSTM)—to pre- dict 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). Figure 1 shows the methodology of processing the Litecoin (LTC) and Ethereum (ETH). Figure 1 shows the methodology of processing the dataset. It starts with data collection, then the data visualization process is used to illus- dataset. It starts with data collection, then the data visualization process is used to illus- trate and explore the data’s behavior and distribution and the relationship between the trate and explore the data’s behavior and distribution and the relationship between the cryptocurrencies. Next, the models are trained with 80% of the collected dataset [42]. The cryptocurrencies. Next, the models are trained with 80% of the collected dataset [42]. The training dataset is from 22 January 2018 until 22 October 2020 and the testing dataset (20% training dataset is from 22 January 2018 until 22 October 2020 and the testing dataset (20% of the data) is form 22 October 2020 until 30 June 2021. Then, after training the models we of the data) is form 22 October 2020 until 30 June 2021. Then, after training the models tested them. Then, we extracted and compared the results and selected the best model we tested them. Then, we extracted and compared the results and selected the best model depending on the daily closing price. depending on the daily closing price. Figure 1. Methodology of processing data and model selection. Figure 1. Methodology of processing data and model selection. AI 2021, 2, FOR PEER REVIEW Figures 2–4 illustrate the training and testing dataset for every targeted cryptocurrency6. Figures 2–4 illustrate the training and testing dataset for every targeted cryptocur- We can see that the price for each currency roughly increased and decreased together along rency. We can see that the price for each currency roughly increased and decreased to- the time-series. gether along the time-series. Figure 2. Training and testing dataset for BTC. Figure 2. Training and testing dataset for BTC. Figure 2 illustrates the BTC closing price within the targeted collected dataset. It shows that the closing price increased gradually until the end of 2020, when the price increased suddenly, it reach a high of 63,381 USD in a top of peak of time series.PDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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