## 5 Results
### Training Situation Analysis  
The model training situation of the nine methods is shown in Table 2. In the experiments, we used instrumentation timing to time the training of the models built by each method.  
In machine learning, the multiple linear regression model benefits from its simple algorithm and obtains the fastest training speed and superior results. On the other hand, the random forest model is the slowest method, but its performance is fair. Presumably, its model pre-construction process is more complex, and we did not conduct any pruning, which resulted in the algorithm becoming too complex, resulting in increased time consumption. The XGBoost and LightGBM models are based on the optimization method of the boosting tree, which is faster than the random forest model, but the effect is relatively poor. In terms of the dynamic journal evaluation task, using the unpruned random forest model is more expensive, but the prediction fit of the journal metric time series dataset is better than that of the XGBoost and LightGBM optimization models.  
The training speed of deep learning models is generally affected by the network structure, the number of epochs, and the number of parameters. Regarding the network structure, the general time consumption in the case of a close number of layers means that ANN is the fastest, CNN is the second fastest, and RNN is the slowest. In terms of the number of epochs, we uniformly conducted 80 epochs of iterative training during the experiment.  
Among the five deep learning models built in this study, the ANN model has only four fully connected layers in its network structure, so it has the fastest training speed despite its large number of parameters. Alternatively, the Conv-1D CNN model has the largest number of trainable parameters (10 times that of WaveNet) among the five methods because it uses more convolutional kernels, but its network structure only contains three convolutional layers and one fully connected layer. The LSTM model and the GRU model are both RNNs, both of which take the longest amount of time to train among the deep learning methods. Although GRU is an optimized and simplified version of the LSTM model, we added two more fully connected layers to the GRU model structure compared with the LSTM model. The relative relationship between the five models' training situations is consistent with our inferences regarding the factors affecting the training speed.  
On the whole, the training time for building models by machine learning methods is shorter than that of deep learning. Considering that machine learning generally requires artificial feature engineering of the input in advance, it is normal that the training time is shorter than that of deep learning models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Methods** & **Training Time** & **Params** & **MSE** \\ \hline \multirow{4}{*}{Machine Learning} & Multiple Linear Regression & **0.36618 s** & - & 0.00044 \\ \cline{2-5}  & Random Forest & 134.23013 s & - & 0.00047 \\ \cline{2-5}  & XGBoost & 3.37131 s & - & 0.00058 \\ \cline{2-5}  & LightGBM & 2.57614 s & - & 0.00076 \\ \hline \multirow{4}{*}{Deep Learning} & ANN & 32.43376 s & 20,161 & 0.00047 \\ \cline{2-5}  & Conv-1D CNN & 51.61289 s & 67,145 & 0.00047 \\ \cline{2-5}  & WaveNet & 73.93935 s & **6721** & 0.00050 \\ \cline{2-5}  & LSTM & 108.72789 s & 13,851 & **0.00037** \\ \cline{2-5}  & GRU & 120.08700 s & 18,561 & 0.00046 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training situation analysis table. The best data in each column are displayed in bold.  
For the models built using the deep learning methods, the variations in the loss for 80 epochs during training are shown in Figure 10. It can be seen in the figure that the loss convergence curves of several models are relatively close, and the LSTM model has the best convergence, while the ANN, Conv-1D CNN, and GRU models have similar convergence effects, and WaveNet has the worst convergence, with large fluctuations at 20-30 epochs.