Network traffic forecasting by support vector machines based on empirical mode decomposition denoising

2012 ◽  
Author(s):  
Yuan Qian ◽  
Jingbo Xia ◽  
Ke Fu ◽  
Rui Zhang
Keyword(s):  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Network Traffic ◽  
Support Vector ◽  
Traffic Forecasting ◽  
Mode Decomposition ◽  
Vector Machines

scholarly journals A Novel Hybrid Network Traffic Prediction Approach Based on Support Vector Machines

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Wenbo Chen ◽  
Zhihao Shang ◽  
Yanhua Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Network Traffic ◽  
Hybrid Network ◽  
Traffic Prediction ◽  
Support Vector ◽  
Swarm Optimization ◽  
Mode Decomposition ◽  
Vector Machines

Network traffic prediction performs a main function in characterizing network community performance. An approach which could appropriately seize the salient characteristics of the network visitors could be very useful for network analysis and simulation. Network traffic prediction methods could be divided into two classes: one is the single models and the opposite is the hybrid fashions. The hybrid models integrate the merits of several single models and consequently can enhance the network traffic prediction accuracy. In this paper, a new hybrid network traffic prediction method (EPSVM) primarily based on Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) is presented. The EPSVM first utilizes EMD to eliminate the impact of noise signals. Then, SVM is applied to model training and fitting, and the parameters of SVM are optimized by PSO. The effectiveness of the presented method is examined by evaluating it with different methods, including basic SVM (BSVM), Empirical Mode Decomposition processed by SVM (ESVM), and SVM optimized by Particle Swarm Optimization (PSVM). Case studies have demonstrated that EPSVM performed better than the other three network traffic prediction models.

Intelligent Fault Diagnosis in Power Plant Using Empirical Mode Decomposition, Fuzzy Feature Extraction and Support Vector Machines

2005 ◽  
Vol 293-294 ◽  
pp. 373-382 ◽  
Author(s):  
Qiao Hu ◽  
Zheng Jia He ◽  
Yanyang Zi ◽  
Zhou Suo Zhang ◽  
Yaguo Lei
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Turbo Generator ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Multi Classification

In this paper, a novel intelligent fault diagnosis method based on empirical mode decomposition (EMD), fuzzy feature extraction and support vector machines (SVM) is proposed. The method consists of two stages. In the first stage, intrinsic mode components are obtained with EMD from original signals and converted into fuzzy feature vectors, and then the mechanical fault can be detected. In the second stage, these extracted fuzzy feature vectors are input into the multi-classification SVM to identify the different abnormal cases. The proposed method is applied to the classification of a turbo-generator set under three different operating conditions. Testing results show that the classification accuracy of the proposed model is greatly improved compared with the multi-classification SVM without feature extraction and the multi-classification SVM with extracting the fuzzy feature from wavelet packets, and the faults of steam turbo-generator set can be correctly and rapidly diagnosed using this model.

scholarly journals Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Yukun Bao ◽  
Tao Xiong ◽  
Zhongyi Hu
Keyword(s):  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
End Effect ◽  
Modeling Framework ◽  
Passenger Traffic ◽  
Straightforward Application ◽  
Mode Decomposition ◽  
Vector Machines

With regard to the nonlinearity and irregularity along with implicit seasonality and trend in the context of air passenger traffic forecasting, this study proposes an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) modeling framework incorporating a slope-based method to restrain the end effect issue occurring during the shifting process of EEMD, which is abbreviated as EEMD-Slope-SVMs. Real monthly air passenger traffic series including six selected airlines in USA and UK were collected to test the effectiveness of the proposed approach. Empirical results demonstrate that the proposed decomposition and ensemble modeling framework outperform the selected counterparts such as single SVMs (straightforward application of SVMs), Holt-Winters, and ARIMA in terms of RMSE, MAPE, GMRAE, and DS. Additional evidence is also shown to highlight the improved performance while compared with EEMD-SVM model not restraining the end effect.

scholarly journals Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion

Sensors ◽  
2008 ◽  
Vol 8 (4) ◽  
pp. 2500-2508 ◽  
Author(s):  
Shaohui Chen ◽  
Hongbo Su ◽  
Renhua Zhang ◽  
Jing Tian ◽  
Lihu Yang
Keyword(s):  
Support Vector Machines ◽  
Image Fusion ◽  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Mode Decomposition ◽  
Vector Machines
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