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AI 2021, 2 483 traveling along the cell state, with the ultimate output being a filtered version of the cell state based on context from the inputs [44]. The LSTM design has been criticized for being ad hoc and for having a large number of components whose purpose is not immediately AI 2021, 2, FOR PEER REVIEW clear. As a result, it is unclear whether the LSTM is the best design, and it is likely that better ones exist [45]. Figure 5 illustrates the structure of a LSTM algorithm [46]. Figure 5. The structure of a long short-term memory (LSTM) algorithm. Figure 5. The structure of a long short-term memory (LSTM) algorithm. The forward training process of the LSTM can be formulated with the following equations: The forward training process of the LSTM can be formulated with the fo equations: O=σ(W·[h ,x]+b) (4) t 0 t−1𝑂=t 𝜎(𝑊0.h,𝑥+𝑏) ht = ot ∗ tabh(Ct)h = 𝑜 ∗ 𝑡𝑎𝑏h(𝐶) (5) ft=σWf ·[ht−1,xt]+bf 𝑓 = 𝜎(𝑊 .h,𝑥+ 𝑏) (1) it=σWf ·[ht−1,xt]+bi (2) where i , o , and f denote the activation of the input gate, output gate, and forget gate, where 𝑖 , 𝑜 , and 𝑓 denote the activation of the input gate, output gate, and for respectively;C andh denotetheactivationvectorforeachcellandmemoryblock,respec- ttt 𝑖=𝜎(𝑊.h,𝑥 +𝑏) C=f∗C +i∗tanh(W ·[h ,x]+b) (3) t t t − 1 t 𝐶 = 𝑓 ∗ 𝐶 c + t − 𝑖 1 ∗ 𝑡 t 𝑎 𝑛 h ( 𝑊c . h , 𝑥 + 𝑏 ) respectively; 𝐶 and h denote the activation vector for each cell and memory b tt tively; and W and b denote the weight matrix and bias vector, respectively. In addition, ◦ spectively; and W and b denote the weight matrix and bias vector, respectively. σ( ) denotes the sigmoid function [47]. tion, 𝜎(°) denotes the sigmoid function [47]. 3.1.2. Bidirectional LSTM (bi-LSTM) bi-LSTM was created by Schuster and Paliwal [48] to train a network utiliz and future input data sequences. The input data are processed using two linke [49]. Bi-directional LSTM predicts or tags the sequence of each element using a f quence based on the context of elements in the past and future. This is the resul LSTMs running in parallel, one from left to right and the other from right to left. T cast of a given target signal is known as composite output. This method has prove g l I i d i tPDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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