DocumentCode
3442114
Title
Notice of Retraction
Improved grey theory-based model in time series adaptive prediction
Author
Xiao-Lei Li ; Jie-Zhong Ma ; Yangming Guo ; Jiang-Yan Sun
Author_Institution
Sch. of Comput. Sci. & Eng., Northwestern Polytech. Univ., Xi´an, China
fYear
2013
fDate
15-18 July 2013
Firstpage
1707
Lastpage
1711
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Forecasting time series accurately is critical to ensure the safety and reliability of complex system. So, time series prediction has been a popular subject. Normally, the information used in time series prediction is always mined from multi-variable time series and small simple data. Thus, based on grey prediction theory, an adaptive prediction model with multi-variable small simple time series data is proposed. In this paper, after analyzing the disadvantages of GM(1,1) model, we modify the initial values and background values of GM(1,1) model, and then the interrelations and characteristics of the multiple variables time series are taken into account. In order to improve the prediction accuracy, we used particle swarm optimization (PSO) to obtain the optimal weight factor ω. At last we proved that the model has good prediction precision by an experiment, which will be useful in applications.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Forecasting time series accurately is critical to ensure the safety and reliability of complex system. So, time series prediction has been a popular subject. Normally, the information used in time series prediction is always mined from multi-variable time series and small simple data. Thus, based on grey prediction theory, an adaptive prediction model with multi-variable small simple time series data is proposed. In this paper, after analyzing the disadvantages of GM(1,1) model, we modify the initial values and background values of GM(1,1) model, and then the interrelations and characteristics of the multiple variables time series are taken into account. In order to improve the prediction accuracy, we used particle swarm optimization (PSO) to obtain the optimal weight factor ω. At last we proved that the model has good prediction precision by an experiment, which will be useful in applications.
Keywords
forecasting theory; grey systems; particle swarm optimisation; queueing theory; time series; GM(1,1) model; PSO; adaptive prediction model; complex system reliability; complex system safety; grey prediction theory; grey theory-based model; multivariable time series; particle swarm optimization; time series adaptive prediction; time series forecasting; Accuracy; Adaptation models; Analytical models; Data models; Fitting; Predictive models; Time series analysis; 1); GM(1; grey prediction; particle swarm optimization; time series adaptive prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 2013 International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4799-1014-4
Type
conf
DOI
10.1109/QR2MSE.2013.6625905
Filename
6625905
Link To Document