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Figure 2. Training and testing dataset for BTC. Figure 2 illustrates the BTC closing price within the targeted collected dataset. It AI 2021, 2 482 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. AI 2021, 2, FOR PEER REVIEW 7 Figure 3. Training and testing dataset for ETH. Figure 3. Training and testing dataset for ETH. Figure 3 shows the ETH closing price within the targeted collected dataset. It demon- strates that the closing price increased gradually until the end of 2020, then the price in- creased suddenly, reaching a high of 4140 USD. Figure 4. Training and testing dataset for LTC. Figure 4. Training and testing dataset for LTC. Figure 42 sihllouwstsratthees LthTeCBcTloCsicnlgospinrigcepwricitehwinithientathrgeettaerdgectoeldlectoeldlecdtaetdasdeat.taItseiltl.usIt- tsrhaotewssththaatththeeclcolosisninggpprircieceinicnrceraesaesdedgrgardaudaulalyllyunutniltitlhtehenedndofo2f0202,0t,hwenhtehnetphreicpericne- cinrecaresaedsesdusdudednelyn,lyr,eiatcrheiancghahiighoff36733,3.6841USD.in a top of peak of time series. Figure 3 shows the ETH closing price within the targeted collected dataset. It demon- 3st.1ra.tMesacthiantetLheearcnlionsginAglgporitchemisncreasedgraduallyuntiltheendof2020,thentheprice increased suddenly, reaching a high of 4140 USD. This section demonstrates three types of machine learning algorithms—long short- Figure 4 shows the LTC closing price within the targeted collected dataset. It illustrates term memory (LSTM), bidirectional LSTM (bi-LSTM), and gated recurrent unit (GRU). that the closing price increased gradually until the end of 2020, then the price increased suddenly, reaching a high of 373.64 USD. 3.1.1. Long Short-Term Memory (LSTM) For various learning issues involving sequential data, recurrent neural networks with 3.1. Machine Learning Algorithms long short-term memory (LSTM) have emerged as an effective and scalable approach. This section demonstrates three types of machine learning algorithms—long short- They are useful for capturing long-term temporal dependencies since they are generic and term memory (LSTM), bidirectional LSTM (bi-LSTM), and gated recurrent unit (GRU). effective [43]. The LSTM is an RNN-style architecture with gates that govern the flow of information between cells. The input and forget gate structures can modify information 3.1.1. Long Short-Term Memory (LSTM) traveling along the cell state, with the ultimate output being a filtered version of the cell For various learning issues involving sequential data, recurrent neural networks with state based on context from the inputs [44]. The LSTM design has been criticized for being long short-term memory (LSTM) have emerged as an effective and scalable approach. ad hoc and for having a large number of components whose purpose is not immediately They are useful for capturing long-term temporal dependencies since they are generic and clear. As a result, it is unclear whether the LSTM is the best design, and it is likely that effective [43]. The LSTM is an RNN-style architecture with gates that govern the flow of better ones exist [45]. Figure 5 illustrates the structure of a LSTM algorithm [46]. information between cells. The input and forget gate structures can modify informationPDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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