Title :
Nonlinear prediction of gross industrial output time series by Gradient Boosting
Author :
Zhang, Rui ; Wang, Hong-li
Author_Institution :
Sch. of Manage., Tianjin Univ., Tianjin, China
Abstract :
Predicting gross industrial production is helpful to design plan in development zone. History data in Jinchuan district, Hohhot, were collected. BDS, Ljung-Box, Box-Pierce, White´s and Teraesvirta´s neural network test and surrogate data test were combined to selecting a proper model. According to phase space reconstruction, function fitting was finished by Gradient Boosting. The results showed that nonlinear dependence existed in series. The production in 2015 was predicted to be 6937977 ten thousand Yuan.
Keywords :
economic indicators; gradient methods; time series; Hohhot; Jinchuan district; development zone; function fitting; gradient boosting; gross industrial output time series; gross industrial production; neural network test; nonlinear dependence; nonlinear prediction; phase space reconstruction; Boosting; Delay; Educational institutions; Indexes; Predictive models; Production; Time series analysis; Gradient boosting machine; gross industrial production; nonlinearity test; phase space reconstruction;
Conference_Titel :
Industrial Engineering and Engineering Management (IE&EM), 2011 IEEE 18Th International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-61284-446-6
DOI :
10.1109/ICIEEM.2011.6035128
