DocumentCode :
1586027
Title :
Time Series Prediction Based on Linear Regression and SVR
Author :
Lin, Kunhui ; Lin, Qiang ; Zhou, Changle ; Yao, Junfeng
Author_Institution :
Xiamen Univ., Xiamen
Volume :
1
fYear :
2007
Firstpage :
688
Lastpage :
691
Abstract :
The application of SVR in the time series prediction is increasingly popular. Because some time series prediction based on SVR wasn ´t very nice in the efficiency of the forecast, this article presents a new regression based on linear regression and SVR. The new regression separates time series into linear part and nonlinear part, then predicts the two parts respectively, and finally integrates the two parts to forecast. Experiments show that the new regression advances the precision of the forecasting compared to the common SVR.
Keywords :
econometrics; prediction theory; regression analysis; support vector machines; time series; linear regression; support vector regression; time series prediction; Additives; Application software; Computer science; Economic forecasting; Fluctuations; Linear regression; Neural networks; Support vector machines; Time series analysis; Weather forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
Type :
conf
DOI :
10.1109/ICNC.2007.780
Filename :
4344279
Link To Document :
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