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AI 2021, 2 484 3.1.2. Bidirectional LSTM (bi-LSTM) bi-LSTM was created by Schuster and Paliwal [48] to train a network utilizing past and future input data sequences. The input data are processed using two linked layers [49]. Bi-directional LSTM predicts or tags the sequence of each element using a finite sequence based on the context of elements in the past and future. This is the result of two LSTMs AI 2021, 2, FOR PEER REVIEW 9 running in parallel, one from left to right and the other from right to left. The forecast of a given target signal is known as composite output. This method has proven to be quite beneficial. The forward function of bi-LSTM with inputs of L units and H as the number of hidden units is calculated using Equations (6) and (7), Figure 6 shows a bidirectional number of hidden units is calculated using Equations (6) and (7), Figure 6 shows a bidi- LSTM structure [50]. The hidden layer of the bi-directional LSTM network saves two rectional LSTM structure [50]. The hidden layer of the bi-directional LSTM network saves values. A participates in the forward calculation, and A transpose is involved in the reverse two values. A participates in the forward calculation, and A transpose is involved in the calculation. The final output value, y, depends on A and A transpose [51]: reverse calculation. The final output value, y, depends on A and A transpose [51]: L H t t t−1 a𝑎==x𝑥w𝑤++ b𝑏w𝑤′ (6) h ∑llh ∑h′ hh l = 1 h ′ , t > 0 , 𝑎 = 𝜃 (𝑎) (7) ath = θhath (7) Figure 6. The structure of a bi-directional LSTM (bi-LSTM) algorithm. Figure 6. The structure of a bi-directional LSTM (bi-LSTM) algorithm. 3.1.3. Gated Recurrent Unit (GRU) zt =σ(Wzxt+Vzht−1+bz) (8) 𝑧 = 𝜎(𝑊𝑥 + 𝑉h + 𝑏) (8) 3.1.3. Gated Recurrent Unit (GRU) Gated recurrent neural networks (Gated RNNs) have demonstrated their effectiveness in a Gvartiedtyreocfuarprepnlticnaetiuornasl nreqtwuiorrinkgs (sGeqatueedntRiaNlNors)tehmavpeodraelmdoantastr[a5t2e]d. tThheeirteraffnescittiivoen- nfuenssctiinonasvianriheitdydoefnaupnpiltiscaotfioGnRsUreaqrueirgiinvgenseaqsufeonlltoiawlsor[5t3e]m:poraldata[52].Thetransition functions in hidden units of GRU are given as follows [53]: r =σ(Wrx +Vrh +br) (9) t t t−1 𝑟 = 𝜎(𝑊 𝑥 + 𝑉 h + 𝑏 ) (9) ht = tanh(Wcxt+ Vc(rt · ht−1)) (10) h = 𝑡𝑎𝑛h(𝑊 𝑥 + 𝑉 (𝑟 .h)) (10) ht =(1−zt)· ht−1+zt · ht (11) h =(1−𝑧).h + 𝑧 .h (11) where the model parameters include all W εRdXd, V εRdXd, and b εRdXd that are shared by where the model parameters include all W𝜖𝑅, V𝜖𝑅, and b𝜖𝑅 that are shared by all time steps and learned during the training stage, (.) denotes the element-wise product, all time steps and learned during the training stage, (.) denotes the element-wise product, and k is a hyper-parameter that represents the dimensionality of hidden vectors. Figure 7 and k is a hyper-parameter that represents the dimensionality of hidden vectors. Figure 7 illustrates the GRU structure [54]: illustrates the GRU structure [54]:PDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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