TIME SERIES PREDICTION BASED ON SUPPORT VECTOR REGRESSION
YANG Jin-fang, ZHAI Yong-jie, WANG Dong-feng, XU Da-ping (North China Electric Power University, Baoding 071003, Hebei Province, China)
This paper introduced the research condition and the basic theory of support vector regression (SVR).SVR is applied to forecast data of Box-Jenkins gas furnace ,and compared with other feedforward network-back-propagate (BP) neural network, self-adaptive expanding neural network. The result of simulation shows that BP neural network and self-adaptive expanding neural network are relatively close in predicting performance , and SVR is obviously superior to these two kinds of methods in predicting performance. The article analyses the reason why SVR is superior to the BP neural network and self-adaptive expanding neural network.
【CateGory Index】： TP183;