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

机译：基于经验模态分解去噪的支持向量机网络流量预测

获取原文

获取原文并翻译 | 示例

页面导航

摘要
著录项
相似文献
相关主题

摘要

Network traffic forecasting plays an important part in network control. A new method based on the Empirical Mode Decomposition (EMD) denoising and Support Vector Machines (SVM) is developed to improve the accuracy of the traffic prediction. Firstly, network traffic data are preprocessed by EMD to remove noise. Then the denoised data are processed by phase space reconstruction to form the training samples. Last the SVM model is constructed to forecast the real network traffic. The results show that the new method is more effective for extracting noise and prediction precision is high.

机译：网络流量预测在网络控制中起着重要的作用。为了提高交通预测的准确性，提出了一种基于经验模式分解和支持向量机的新方法。首先，通过EMD对网络流量数据进行预处理以去除噪声。然后，通过相空间重构处理去噪数据，以形成训练样本。最后，构建SVM模型以预测实际的网络流量。结果表明，该方法对噪声的提取更为有效，预测精度较高。

著录项

来源
《Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on》|2012年|p.3327- 3330|共4页
会议地点 Three Gorges(CN)
作者
Qian Yuan;
展开▼
作者单位

Telecommunication Engineering Institute, Air Force Engineering University, Xi'an China;

展开▼
会议组织
原文格式 PDF
正文语种 eng
中图分类通信;
关键词

相似文献

外文文献
中文文献
专利

1. A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis [J] . Dongxiao Niu, Shuyu Dai Energies . 2017,第3期

机译：改进的粒子群优化算法和最小二乘支持向量机的经验模态分解和灰色关联度去噪短期负荷预测模型
2. Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method [J] . Bao Y., Xiong T., Hu Z. Discrete dynamics in nature and society . 2012,第Pta3期

机译：集成经验模态分解和基于斜率的支持向量机预测航空客运量
3. Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method [J] . YukunBao, TaoXiong, ZhongyiHu Discrete dynamics in nature and society . 2012,第4期

机译：集成经验模态分解和基于坡度的支持向量机预测航空客运量
4. Network traffic forecasting by support vector machines based on empirical mode decomposition denoising [C] . Qian Yuan International Conference on Consumer Electronics, Communications and Networks . 2012

机译：基于经验模式分解去噪的支持向量机网络流量预测
5. Rethinking Early Warning Systems: Using the Radial Based Support Vector Machine to Forecast Currency Crises [D] . McMahon, Matthew John. 2019

机译：重新思考预警系统：使用基于径向的支持向量机预测货币危机
6. Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine [O] . Maram A. Wahba, Amira S. Ashour, Sameh A. Napoleon, 2017

机译：基于二次支持向量机的经验模式分解和纹理特征相结合的皮肤病变分类
7. Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm [O] . Shuyu Dai, Dongxiao Niu, Yan Li 2018

机译：基于改进灰狼优化算法优化的自适应噪声和支持向量机的完全集合经验模式分解的日峰值负荷预测

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

摘要

著录项

相似文献

相关主题

期刊订阅