## 5 Results
### Evaluation Metrics Results  
In Section 4.1, we introduce the mainstream evaluation metrics for time series prediction selected for this experiment, and the results of the evaluation metrics for the models built by the nine methods are shown in Table 3.  
Among the models built by machine learning methods, the multiple linear regression model and the random forest model have the best metrics. The multiple linear regression model exhibits low training time consumption and high metric results with regard to the journal metrics time series dataset applied in this study and has the best overall performance among the machine learning models in this study, indicating that this dataset can be used to mine better linear dependencies. The random forest model is able to learn the possible relationships between a large amount of time series data via the use of a forest composed of decision trees--without the use of a pruning strategy--and although the training period is long, time-consuming, and more complex in structure, it also obtains almost the best results in terms of the evaluation metrics in the journal time series dataset. However, its overall performance is not as good as the multiple linear regression model due to the long training period and excessive memory usage. Figure 10: Comparison of deep learning methods (80 epochs; loss descent). The XGBoost and LightGBM models are based on the optimization method of boosting trees, and both models are slightly worse than the random forest model. LightGBM is the model with the worst evaluation metrics in terms of machine learning, and although it optimizes XGBoost from a theoretical point of view, it exhibits poor prediction generalization in this study, which is also consistent with its possible disadvantage of easy overfitting. Both XGBoost and LightGBM greatly improve the speed of training with a slight decrease in accuracy compared to the random forest model. The performance of the test evaluation metrics of the four machine learning models after training on the journal metrics time series dataset reflects that the dataset constructed in this study achieved superior feature engineering and can be better applied to many time series analysis methods in the field of machine learning. Among the models built by deep learning methods, the LSTM model is optimal in all metrics and is the best deep learning model when considering the training situation. However, the GRU model outperforms the other three deep learning models in terms of overall metrics, considering that both are special RNNs with gating units added, which can indicate that the ability of RNNs to mine implied relationships through short-time series under the journal metrics time series dataset is stronger, and the ability to link covariates with other time series for learning is more suitable for the dynamic journal evaluation task proposed in this paper. The ANN and CNN models perform similarly on the evaluation metrics. ANN models are able to fit nonlinear relationships due to the presence of activation functions, and their logic and performance in the field of time series data analysis are similar to the multiple linear regression model used in machine learning. The large number of neurons contained in multiple hidden layers introduces a large number of parameters to be learned but also improves the models' ability to mine the implicit relationships in time series data. The Conv-1D CNN model contains a large number of one-dimensional convolutional kernels and thus has the largest number of parameters, but it is able to extract multimodal hidden features among the time series of journal metrics compared to the ANN model, which contains only fully connected layers; therefore, evaluation metrics perform better with the Conv-1D CNN model than the ANN model overall.