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

fYear

2014

fDate

19-21 Aug. 2014

Firstpage

369

Lastpage

373

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2014 11th International Conference on

Conference_Location

Xiamen

Print_ISBN

978-1-4799-5147-5

Type

conf

DOI

10.1109/FSKD.2014.6980862

Filename

6980862