TY - GEN
T1 - Notice of Retraction
T2 - 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2013
AU - Li, Xiao Lei
AU - Ma, Jie Zhong
AU - Guo, Yangming
AU - Sun, Jiang Yan
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - 1)
KW - GM(1
KW - grey prediction
KW - particle swarm optimization
KW - time series adaptive prediction
UR - http://www.scopus.com/inward/record.url?scp=84890109695&partnerID=8YFLogxK
U2 - 10.1109/QR2MSE.2013.6625905
DO - 10.1109/QR2MSE.2013.6625905
M3 - 会议稿件
AN - SCOPUS:84890109695
SN - 9781479910144
T3 - QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
SP - 1707
EP - 1711
BT - QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
PB - IEEE Computer Society
Y2 - 15 July 2013 through 18 July 2013
ER -