## 4 Methods
### Deep Learning Methods
#### 4.2.4 Long Short-Term Memory Network (LSTM)  
The long short-term memory network (LSTM) is a recurrent neural network (RNN) specifically designed to solve the problem of gradient disappearance and gradient explosion caused by the existence of long-term dependence in general RNN, which introduces a gating unit with 3 gates (as shown in Figure 8), where \(h_{t-1}\) represents the output of the previous cell, \(C_{t-1}\) represents the previous cell state, \(x_{t}\) represents the input of the current cell, \(\widetilde{C}_{t}\) represents the information to be updated into the cell state by the current cell, \(h_{t}\) represents the output of the current cell, \(C_{t}\) represents the current cell state, and \(\sigma\) represents the sigmoid layer (capable of mapping data in the 0-1 range and acting as a gating signal).  
Figure 8: Example of LSTM cell, after inputting \(x_{t}\) and processing, obtaining the output \(h_{t}\).  
Figure 7: Example of atrous convolution. The circles represent the data of each layer, and the dotted lines represent the convolution operations between layers. The solid lines reflect atrous convolution, with different expansion factors between layers.  
The LSTM model cell can be roughly divided into two lines, with the top line controlling long-time memory and the bottom line controlling short-time memory.  
In the oblivion gate, \(f_{t}\) reads \(h_{t-1}\) and \(x_{t}\) and outputs a number from 0-1 with the previous cell state \(C_{t-1}\) in the previous cell, and the value determines which information is discarded from \(C_{t-1}\).  
In the input gate \(i_{t}\), the results obtained by multiplying the results of the sigmoid layer with the vectors generated by the _tanh_ layer are used to determine which new information \(\widetilde{C}_{t}\) is added to the cell state, and the final combination of the results of the forgetting gate and the input gate causes the cell state to be updated to \(C_{t}\).  
In the output gate \(o_{t}\), the cell state is processed using the tanh layer \(C_{t}\) and multiplied with the sigmoid layer result to obtain the final output of this cell, \(h_{t}\); then, \(h_{t}\) and \(C_{t}\) are passed to the next cell [44].  
We can abstract the _LSTM_ model as a function with three inputs and one output, as shown in Equation (10).  
\[h_{t}=LSTM(x_{t},\;C_{t-1},\;h_{t-1}) \tag{10}\]  
In this study, the dynamic journal evaluation task only required a one-step prediction of the possible future impact factors of journals. To simplify the network structure, we set only one layer of _LSTM_ and one layer of fully connected layers when building the LSTM model. In the _LSTM_ layer, the matrix dimension of the hidden layer was set to 50, the activation function was set to ReLU, the inputs were the spliced (\(3\times 18\)) journal metrics time series data, and only the output of the last time series was returned. The fully connected layer processed the 50-dimensional output of the _LSTM_ layer into a single output value and this output was the one-step prediction of the input time series sample; we chose the MSE metric as the main loss function and the Adam algorithm as the optimizer. Finally, the training, validation, and testing of the _LSTM_ model on the journal metrics time series dataset were completed, and the evaluation metrics results were derived.  
_LSTM_ controls the transmission state by gating the state and can remember information that needs to be remembered for a long period of time while forgetting unimportant information. Therefore, the _LSTM_ model we built can be applied to the multivariate short-time multi-series data-based time series prediction task in this study.