Title :
A hybrid method for short-term sensor data forecasting in Internet of Things
Author :
Peng Ni ; Chunhong Zhang ; Yang Ji
Author_Institution :
Sch. of Inf. & Commun. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
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.
Keywords :
Internet of Things; data handling; forecasting theory; particle swarm optimisation; regression analysis; sensors; support vector machines; EEMD; Internet of Things; PSO algorithm; SVR; ensemble empirical mode decomposition; particle swarm optimization algorithm; sensor data prediction; short-term sensor data forecasting; support vector regression; Accuracy; Algorithm design and analysis; Forecasting; Internet of Things; Prediction algorithms; Support vector machines; Time series analysis; Ensemble Empirical Mode Decomposition (EEMD); Internet of Things; Particle Swarm Optimization (PSO); Sensor data forecasting; Support Vector Regression (SVR); Time series analysis;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2014 11th International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4799-5147-5
DOI :
10.1109/FSKD.2014.6980862
