# Research on a Service Load Prediction Method Based on VMD-GLRT
## 3 Result and Discussion
### Experiments and Results
#### 3.2.2 Comparative Experiment  
Based on the previous analysis, this paper lists the existing combination models for experiments and comparison from three aspects of statistical methods, machine learning, and deep learning. To verify the effectiveness of the model, this paper will test and compare other prediction models in the field and prediction methods in service load prediction. The prediction result graph is shown in Figure 6 below.  
Figure 6 shows that the prediction results of each model can be consistent with the primary trend under the premise of the same environment and setting. Comparing the proposed model with its base reference model, GRU-LSTM, the MAPE phase is improved by about 2%. To verify the model's generalization ability, the model in this paper is predicted and compared with CNN-BiLSTM, ARIMA-LSTM, and Attention-TCN, which are novel models in recent studies from two aspects of statistical methods and deep learning methods. It can be seen from the results that the model proposed in this paper has a better prediction effect on the whole and is superior to other models in terms of trend and accuracy.  
To further illustrate the prediction performance and effect of the model, this paper evaluates the model based on the four evaluation indicators of MSE, RMSE, MAE, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline GRU-LSTM [16] & 19.8621 & 4.4567 & 3.2288 & 5.06 \\ GLSTM-RS & 19.7945 & 4.4491 & 3.223 & 5.0497 \\ GLSTM-RT & 19.6048 & 4.4277 & 3.2036 & 5.0262 \\
**VMD-GLRT** & **7.2306** & **2.7075** & **1.9393** & **3.0494** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation indexes of ablation experiments.  
Figure 6: Model prediction results for Comparative experiments.  
MAPE, and the results of each item are shown in Table 3 below. The bold one is the algorithm of this paper, and the underlined one is the optimal result.  
Compared with the GRU-LSTM model proposed in the literature [27], the model presented in this paper has dramatically improved in all indicators. It can be seen from the data in the table that compared with the evaluation index results obtained by the recent model on the validation set, the prediction model proposed in this paper can achieve a more accurate performance. The study in [25] uses three proportions of equal weight average, differential reciprocal, and objective assignment to merge the prediction results of the combined model and obtain the optimal result. This paper compares the optimal results obtained under the differential typical weight setting method. The results show that the MAPE of this prediction method is increased by about 2.4% compared with this result. The MSE is reduced by about 13.5538, reducing the model's error rate. Compared with the prediction results of the study in [26], the MAPE of this paper is reduced by about 3%, and the MSE is reduced by about 18.036, which dramatically improves its accuracy. On the same data set, compared with the method of visualizing time series and extracting and predicting features through CNN and LSTM proposed by the study in [23], this paper uses the same data set for prediction and comparison, and it can be seen that the proposed model in this paper reduces MAE by about 1.8837. It can be seen that the prediction results of the proposed model are closer to the actual value.  
Through the above analysis, the model in this paper is superior to the existing statistical prediction methods and deep learning prediction methods in terms of evaluation indicators. In actual situations, the model can effectively deal with the differences in service load characteristics of different software in different scenarios and provide a reliable service load prediction scheme to help the industry predict the load demand of various services in different application scenarios in the future, provide response preparation for software practitioners, and make policy warnings in advance.