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

Volume

Part 1

fYear

2011

fDate

3-5 Sept. 2011

Firstpage

153

Lastpage

156

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICIEEM.2011.6035128

Filename

6035128