## 4 Methods
### Machine Learning Methods
#### 4.1.2 Random Forest  
The method is essentially an integrated learning method; it integrates many decision trees into a forest and uses the average values of the predicted results to predict the final outcome. The random forest method is insensitive to multivariate covariance, is more robust to missing data and unbalanced data, and can predict the effects of up to several thousand explanatory variables well, even when the input data are not normalized [42]. In the journal metrics time series dataset, the \(X\) vector is the independent variable of the input model (a journal with three years of metrics data stitched together with a length of 54), \(Y\) is the dependent variable to be predicted, the training set \(\varphi\) is randomly generated in (\(X\), \(Y\)), the predicted result is set to \(f\) (\(X\)), and the mean square generalization error is \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Existing Studies of Time Series Forecasting Tasks** \\ \hline Multiple Linear Regression & [PERSON], [PERSON] use Multiple Linear Regression time series model to forecast PM10 concentrations in Peninsular Malaysia [32]. \\ \hline Random Forest & [PERSON], [PERSON], [PERSON] [PERSON], et al. use Random Forest time series model to predict avian influenza H5N1 outbreaks [33]. \\ \hline XGBoost & [PERSON], [PERSON], [PERSON], et al. use machine learning models (XGBoost and LightGBM) to predict the residual value of heavy construction equipment [34]. \\ \hline ANN & [PERSON], [PERSON], [PERSON], et al. use ANN to predict housing prices [35]; [PERSON], [PERSON], [PERSON], et al. train ANN to predict stock price index [36]. \\ \hline Conv-1D CNN & [PERSON], [PERSON], [PERSON] use Conv-1D CNN model in deep learning to forecast day-ahead building-level load [37]. \\ \hline WaveNet & [PERSON], [PERSON], [PERSON] use WaveNet to forecast conditional time series [38]. \\ \hline LSTM & [PERSON], [PERSON], [PERSON], et al. integrate LSTM and Random Forest to estimate soil moisture [39]. \\ \hline GRU & [PERSON], [PERSON], [PERSON] use GRU to forecast time series data with Bitcoin’s price dataset as their time series dataset [40]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief discussions of how existing studies apply these methods to time series forecasting tasks. \(E_{x,y}[Y-f(X)]^{2}\). The final regression function of the random forest method is shown in Equation (9). \[Y=E_{\varphi}f(X,\ \varphi) \tag{9}\]  
In our experiments, we set the maximum number of iterations of the weak learner to 500 and could use all of the feature values for partitioning when generating the decision tree. We also used the bootstrap method to randomly select the sample set with a release (the random forest is a bagging type algorithm), and each sample set grew into a single decision tree with each leaf node containing one prediction. We chose to use the mean square error (MSE) metric \(\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}\) with the L2 regularization term as the loss function and a measure of branch quality, and the selection of features was based on the difference in the value of this function between the father and leaf nodes. Considering that the dynamic journal evaluation task can be understood as a regression task under supervised learning, we divided the whole feature space \(X\) into many non-overlapping regions and used its mean value as the predicted result for all samples in the same region, hoping to find a way in which to divide the regions so that the loss was as low as possible. For the partitioning scheme, we used recursive slicing to exhaust the possible partitioning thresholds for each feature when branching in order to find the optimal slicing feature and the optimal slicing point threshold. We stopped when the branching reached the preset terminating conditions (e.g., the upper limit of the number of leaves, loss threshold, etc.). When making predictions, the means of all the results of the decision tree predictions on the input variables were used as the final prediction results.