## 3 FINM Prediction Model
### BiLSTM Module  
The LSTM is a special feedback neural network designed for processing time series data. The core contains the accumulation function and gating mechanism. These mechanisms enable LSTM neurons to replicate and accumulate the true value of their own state and external input signals, which is very suitable for solving deep learning problems related to time series data. The LSTM networks consist of an input layer, a hidden layer, and an output layer. The hidden layer uses a gating mechanism to selectively control the flow of information. This mechanism includes a forgetting gate, an input gate, and an output gate, which are responsible for forgetting unwanted information, selectively memorizing new information, and controlling the final output of information. These gating mechanisms work together on the LSTM unit, so that it can more realistically characterize and simulate the internal characteristics, and effectively combine long-term memory information with short-term memory information. The forgetting gate determines how much information in the previous unit state needs to be forgotten through the activation function. The formula is as follows:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},\ x_{l}]\Big{)}+b_{f} \tag{4}\]  
\[c^{\prime}_{t-1}=f_{t}\cdot c_{t-1} \tag{5}\]  
Here, \(f_{t}\) is the output of the forgetting gate, \(W_{f}\) is the weight of the forgetting gate, \(h_{t-1}\) is the hidden state of the previous moment, \(x_{t}\) is the input value of the current moment, \(b_{f}\) is the bias term of the forgetting gate, and \(\sigma\) is the activation function. \(f_{t}\) is the forgetting coefficient of \(c_{t-1}\); when \(f_{t}=0\), we completely forget the information of \(c_{t-1}\), and when \(f_{t}=1\), we completely retain the information of \(c_{t-1}\). \(c_{t-1}^{\prime}\) is the unit state that retains information at the previous moment; \(b_{f}\) is the threshold value. \[i_{t}=\sigma(W_{i}\cdot[h_{t-1},\ x_{t}]+b_{i}) \tag{6}\]  
\[\overline{c_{t}}=\tanh\ (W_{c}\cdot[h_{t-1},\ x_{t}]+b_{c}) \tag{7}\]  
\[c_{t}=i_{t}\cdot\overline{c_{t}}+f_{t}\cdot c_{t-1} \tag{8}\]  
Here, \(i_{t}\) is the output of the input gate, \(W_{i}\) is the weight of the input gate, and \(b_{i}\) is the offset of the input gate; \(\overline{c_{t}}\) is the candidate unit state, \(W_{c}\) is the weight of the candidate unit state, and \(b_{c}\) is the bias term of the candidate unit state. \(c_{t}\) is the superposition information of the forgotten gate and the input gate after processing. \[o_{t}=\sigma(W_{o}\cdot[h_{t-1},\ x_{t}])+b_{o} \tag{9}\]  
\[h_{t}=o_{t}\cdot\tanh\ (c_{t}) \tag{10}\]  
\[\gamma=\sigma(W_{\gamma}\cdot h_{t}+b_{\gamma}) \tag{11}\]  
Here, \(o_{t}\) is the output of the output gate, \(W_{o}\) is the weight of the output gate, and \(b_{o}\) is the offset of the output gate; \(h_{t}\) is the hidden state of the current moment, \(c_{t}\) is the unit state of the current moment, which is determined by the forgetting gate and the input gate, and \(\gamma\) is the predicted output value; \(W_{\gamma}\) is the weight; and \(\hbar\) is the threshold value. However, the traditional one-way LSTM can only mine the feature information of time series data from the past to the future in airport express passenger flow prediction and cannot directly explore the potential passenger flow change trend from the future to the past. In order to overcome this limitation, the integrated BiLSTM in this paper consists of two ordinary LSTM layers, which process forward and reverse time series data, respectively. This design enables the BiLSTM to predict current traffic using both past and future information. Specifically, the forward LSTM layer mines the feature information of time series data from the past to the future, while the reverse LSTM layer mines from the past to the future. In this way, the BiLSTM can more fully understand the complex patterns in passenger flow data and significantly improve the prediction accuracy of the model.