## 3 Methodology
### Feature Selection Method
\tag{12}\]  
For any target point \(x\), the interpolation result is  
\[y(x)=a_{k}x^{2}+b_{k}x+c_{k} \tag{13}\]  
After generating daily data using the above formula, calculate the total difference and the corresponding scaling factor for each month to adjust the daily value. Assuming that interval \([x_{start},x_{end}]\) corresponds to raw data \(y_{total}\), the calibration formula is as follows:  
\[y_{calibration}(x)=\frac{y_{total}}{\sum_{x=x_{start}}^{x_{end}-x_{start}}y(x)} \cdot\ y(x) \tag{14}\]  
Step 6. Weight assignment  
In neural networks, when the dimensional difference between input data is significant, models tend to focus solely on indicators with large values while overlooking some crucial ones with relatively small values. Data normalization is the process of scaling data proportionally to a specific range, which is mainly used to eliminate dimensional differences and thereby enhance a model's performance. Therefore, data normalization is generally required before feeding the data into the neural network. Here, the min-max normalization method is adopted and the specific formula is as follows:  
\[X_{norm}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{15}\]  
where \(X_{norm}\) is the normalized value, \(X_{\text{min}}\) is the minimum value in a set of data, and \(X_{\text{max}}\) is the maximum value in a set of data. The data are effectively linearly mapped onto the [0, 1] interval through Min-Max normalization. The weight allocation methodology is implemented through correlation-based analysis and k-means. During data normalization, indicators are ranked and grouped according to their correlation coefficients with container throughput, assigning higher weights to strongly correlated groups. The input matrix obtained after assigning weights is as follows:  
\[\left(X_{(1,1)},X_{(2,2)},\ \ \ldots\,X_{(k,m)}\right)=\left[ \begin{array}{ c c c}X_{11}&X_{12}&\ldots&X_{1m}\\ X_{21}&X_{22}&\ldots&X_{2m}\\ \ldots&\ldots&\ldots&\ldots\\ X_{n1}&X_{n2}&\ldots&X_{nm}\end{array} \right] \tag{16}\]  
where \(m\) represents the number of indicators, \(k\) represents the number of groups sorted by relevance, \(X_{(k,m)}\) represents the \(m\) indicator and the \(k\) group, and \(n\) represents the total number of days of data for each indicator. In the formula, \(X_{11},X_{12},X_{21},\ \ldots\,X_{mn}\) are represented as  
\[ \begin{array}{c}X_{11}=\frac{\omega_{1}(X-X_{\min})}{X_{\max}-X_{\min}}\\ X_{21}=\frac{\omega_{1}(X-X_{\min})}{X_{\max}-X_{\min}}\\ X_{12}=\frac{\omega_{2}(X-X_{\min})}{X_{\max}-X_{\min}}\\ \ldots\\ X_{mn}=\frac{\omega_{k}(X-X_{\min})}{X_{\max}-X_{\min}}\end{array}  \tag{17}\]  
where \(\omega_{1},\omega_{2},\ \ldots\,\omega_{k}\) represent the weight multiplied by each group during normalization, from the first group to the \(k\) group. For the calculation of weights, we determine the final weight size based on the summed percentage of Pearson as well as Spearman correlations for each group after grouping. The specific formula is as follows:  
\[\omega_{k}=\frac{\sum_{i=1}^{m}\left(\gamma_{i}+\rho_{i}\right)}{\sum_{i=1}^{m }\left(\gamma_{i}+\rho_{i}\right)} \tag{18}\]  
where \(n\) is the number of indicators in group \(k\) and \(m\) is the total number of indicators. Through the above calculation, we can obtain the specific weights of each correlation group.