## 5 Experiments and Results  
### Experimental Setup  
In this study, we examine the performance of three distinct 5G vertical applications, namely the Internet of Vehicles, Industrial Internet, and intelligent manufacture. The data are collected from the practical operation of the experimental 5G network for the three aforementioned typical vertical applications, which are the most important scenarios of 5G vertical applications for China Unicom that can provide enough network data for generating high-quality deep learning models. Moreover, unlike the conventional evaluation methods by generating data via software simulation, our experiment results are more convincing. Finally, our three scenarios studied herein have diverse requirements in terms of user experiences and network quality, and thus our experiment results can cover most of typical 5G vertical applications, which can provide more useful insights for the deployment potential of our proposed scheme. To this end, we obtained a total of 10,500 data samples from each of the three scenarios for seven days, of which the first 80% were used as the training dataset and the remaining data were used as the test dataset, then utilized to evaluate the quality detection and forecasting performance. We select several metrics with the highest correlation from the derived multi-metric data, and process the multi-dimensional time series data to serve as input data for quality monitoring. Specifically, concerning the 5G key performance indicators (KPIs), we focus on the airport traffic, wireless resource utilization, average user uplink/downlink rates, CQI, RRC connection success rate, and the packet loss rate.  
For the parameters in the anomaly detection algorithm, the window data length is set at 12 and the number of epochs is set at 100. Additionally, the parameters \(\alpha=0.5\) is also utilized. When performing tests, we search for the optimal threshold \(\lambda\) by utilizing the ROC curve. We note that the optimal value is crucial in terms of enhancing the overall performance of the model. The LSTM model is a three-layer neural network. The first two layers are with 64 and 32 neurons, respectively, and a fully connected layer is employed as the output layer. The ReLu function is used as the activation function. During the training process, the size of the mini-batch and learning rate are set to 32 and 0.001, respectively.  
To assess the efficacy and generalization of our proposed approach, we conducted an anomaly detection experiment on network data in three distinct settings. The performance evaluation of our method was based on three well established metrics: precision (\(P\)), recall (\(R\)), and F1-score (\(F1\)). Among these metrics, precision denotes the fraction of actual anomaly samples identified as such by our method, while recall indicates the fraction of true anomaly samples correctly classified as \"abnormal\" by our algorithm. To provide a comprehensive picture of the detection performance, we further calculated the F1-score, which balances precision and recall. The use of these evaluation metrics enabled us to quantitatively gauge the effectiveness and robustness of our method across different scenarios. They can be represented as  
\[P=\frac{TP}{TP+FP},\quad R=\frac{TP}{TP+FN},\quad F1=2\cdot\frac{P\cdot R}{P+R} \tag{22}\]  
where \(TP\) denotes true positives; \(FP\) denotes false positives; and \(FN\) denotes false negatives.