## 5 Experiments and Results
### Performance of the Proposed Anomaly Forecasting Method  
In this subsection, we evaluate the performance of the proposed anomaly forecasting method in terms of accuracy, using the F1-score as the evaluation metric. To examine the advantages of the proposed anomaly forecasting method, we apply it to forecast anomalies in network data from three different 5G vertical applications. Table 3 presents the test accuracy for datasets obtained from the Internet of Vehicles, intelligent manufacturing, and the Industrial Internet. We compare the proposed anomaly forecasting method with a baseline approach using historical network data as network data at future periods for anomaly detection. It can be observed that the accuracy of the predicted results is comparable to the baseline in most cases, and in some instances, it may even surpass the baseline approach. In this subsection, Table 3 elucidates the test accuracy for datasets hailing from the Internet of Vehicles, intelligent manufacturing, and Industrial Internet verticals. In our study, we juxtapose the proposed anomaly forecasting method with a baseline approach that directly harnesses real data for anomaly detection. Upon the close inspection of Table 3, we can observe that, in most instances, the accuracy of the forecasted results closely aligns with, if not outperforms, the baseline approach. This underscores the effectiveness of the proposed anomaly forecasting method, as it showcases an ability to yield accuracy levels that are not just on par with, but occasionally surpass, the performance of the baseline approach. These results are quite encouraging, suggesting that the proposed anomaly forecasting method holds promise in enhancing anomaly detection in 5G vertical applications. The method's ability to consistently match or outdo the accuracy of the baseline approach underlines its potential as a valuable tool for 5G network data anomaly detection and forecasting. However, it is worth noting that the effectiveness of the anomaly forecasting method could vary depending on the specific characteristics of the dataset and the nature of the anomalies present, necessitating further investigations into its applicability across a wider range of scenarios. In order to provide a more explicit evaluation of our proposed anomaly prediction method, we conducted a 24-hour-per-day case study aimed at highlighting the differences in the anomaly scores obtained based on real versus predicted data. Figures 6-8 depict the anomaly scores for anomaly detection using real or predicted network data from the Internet of Vehicles, intelligent manufacturing, and Industrial Internet, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**F1**} \\ \cline{2-3}  & **The Proposed scheme** & **The Baseline Scheme** \\ \hline Internet of Vehicles & 0.912 & 0.701 \\ Intelligent manufacturing & 0.899 & 0.687 \\ Industrial Internet & 0.869 & 0.669 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of the proposed network anomaly forecasting method for three 5G vertical application. Figure 6: The anomaly score of network anomaly forecasting in the Internet of Vehicles. Figure 7: The anomaly score of network anomaly forecasting in intelligent manufacturing. A closer look at these results shows that the performance of anomaly prediction results is comparable to direct anomaly detection. However, it is important to note that anomaly warnings can be subject to false detections. As shown in Figure 6, the Internet of Vehicles shows that in some instances, the anomaly scores of both methods spike dramatically. These instances represent potential anomalies in the system, highlighting the ability of anomaly prediction and anomaly detection to flag these instances. In contrast, in other cases, we can observe that one method may flag potential anomalies while the other does not, indicating a difference in the detection capabilities of the two methods.