A hybrid method for short-term sensor data forecasting in Internet of Things

摘要：

　　In this paper,the hybrid method is proposed for sensor data predicting in the Internet of Things which combining the Ensemble Empirical Mode Decomposition(EEMD),Support Vector Regression(SVR),Particle Swarm Optimization(PSO)algorithm.The proposed hybrid method is examined by several kinds of sensor data.The obtained results confirm the universality,generality and high forecasting accuracy of the hybrid method.

关键词： Sensor data forecasting Time series analysis Ensemble Empirical Mode Decomposition (EEMD) Support Vector Regression (SVR) Particle Swarm Optimization (PSO) Internet of Things

作者: Peng Ni Chunhong Zhang Yang Ji

作者单位: School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing,China

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 377-381

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）