# Research on a Service Load Prediction Method Based on VMD-GLRT
### Related Work
#### 1.2.2 Deep Learning  
Deep learning methods have seen rapid growth in time series modeling, with many researchers taking advantage of their powerful capabilities and ability to automatically extract features to cope with time series. The study in [13] proposed a load prediction method based on LSTM to improve network speed. This method considered the problem of insufficient resources in Edge Data Centers (EDC) and combined it with user mobility and geographical location information to improve prediction accuracy. Compared with the traditional statistical method, the accuracy of the proposed method increased by 4.21% on average. The study in [14] proposed a cloud workload prediction algorithm, L-PAW, based on deep learning. The model integrated the designed TSA and gated recurrent unit into RNN to achieve adaptive and accurate prediction of highly variable workloads. L-PAW achieves superior performance. The study in [15] proposed an integrated prediction method combining Bidirectional and Grid Long Short-Term Memory networks (BG-LSTM) to predict workload conditions and resource usage records. The Savitzky-Golay filter smoothed the model to eliminate noise. Experiments show that the model has good performance and adaptability. The study in [16] proposed a prediction model, CPW-EMC, based on a noise reduction algorithm and neural network, which improved the robustness of the prediction algorithm through noise reduction, used a multidimensional prediction network, and comprehensively evaluated the model combined with the CMES evaluation standard. Compared with other popular methods, the performance was improved by up to 17%. The study in [17] established a deep learning module, SG-CBA, based on the Convolutional Neural Network (CNN) and Long Short-Term Memory network (BiLSTM) and integrated the filter and attention mechanism. Experiments were carried out on the accurate public data set of Alibaba. The results showed that compared with the traditional and more popular models, it could achieve better performance. Inspired by the translation of the long text, the study in [18] proposed a technology based on Attention-seq2 seq to predict a load of cloud resources, which solved the problem of RNN forgetting critical information in long-term sequences and tested it on public data sets. Compared with other models, the accuracy was improved while the training time on the model was reduced, which showed the effectiveness of the current model. The study in [19] used the combination of GRU and LSTM to model the time series of node load data, selected the combination data of CPU and memory usage through the random forest, and used the combination model to predict it. The results showed that compared with traditional statistical methods, single deep learning methods, and combined prediction methods, the proposed method achieved good performance in prediction results. The study in [20] proposed a combined load forecasting method based on IF-EMD-LSTM (IEBL), which integrated the Isolation Forest algorithm (IF) and Empirical Mode Decomposition (EMD) into LSTM to improve the accuracy of forecasting. The experimental results showed that the error of the combined forecasting model was reduced by up to 18.72%. The prediction accuracy was higher than its corresponding single prediction model. Many problems can be solved for time-series prediction: for example in the literature [21; 22; 23], where several prediction methods were proposed for a load of services. Additionally, under other application areas, for example in the literature [24; 25; 26], prediction methods have been proposed for PM (2.5), freight volumes, and so on. From the above analysis, it can be concluded that the combined prediction model always outperforms the single prediction model in terms of performance, both in terms of machine learning and deep learning. Moreover, many of the prediction models currently proposed for cloud computing load problems have improved performance compared to traditional prediction models, but their overall accuracy needs to be improved.