Predicting Beijing's tertiary industry with an improved grey model

Wang, Q; Liu, L; Wang, S; Wang, JZ; Liu, M

Predicting Beijing's tertiary industry with an improved grey model

Wang, Q Liu, L Wang, S Wang, JZ Liu, M

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Publication Type:: Journal Article
Citation:: Applied Soft Computing Journal, 2017, 57 pp. 482 - 494
Issue Date:: 2017-08-01

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Field	Value	Language
dc.contributor.author	Wang, Q	en_US
dc.contributor.author	Liu, L	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Wang, JZ	en_US
dc.contributor.author	Liu, M https://orcid.org/0000-0003-4256-6531	en_US
dc.date.issued	2017-08-01	en_US
dc.identifier.citation	Applied Soft Computing Journal, 2017, 57 pp. 482 - 494	en_US
dc.identifier.issn	1568-4946	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126707
dc.description.abstract	© 2017 Elsevier B.V. In the context of the growth slowdown in China, it is important to accurately forecast the future economic trend to guide policy-makers the direction of adjusting their current economic policies. In this paper, we intend to predict Beijing's tertiary industry, whose datasets are small, irregular and non-stationary, leading to a difficulty of building an accurate prediction model. To this end, we present an improved grey model, named PRGM(1,1), which extends the grey prediction model by integrating two techniques, i.e., the particle swarm optimization algorithm for parameter optimization and the exponential preprocessing method for data cleaning. The experimental results show that PRGM(1,1) outperforms other variants of the grey prediction model in predicting Beijing's tertiary industry, and is viable to do reasonable prediction over short and fluctuated economic data sequences. In addition, we employ PRGM(1,1) in the economic prediction of Beijing's tertiary industry in the next five years, and conclude that the growth rate will decelerate. Our prediction result seems to be in line with the economic slowdown in China this year.	en_US
dc.relation.ispartof	Applied Soft Computing Journal	en_US
dc.relation.isbasedon	10.1016/j.asoc.2017.04.022	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Predicting Beijing's tertiary industry with an improved grey model	en_US
dc.type	Journal Article
utslib.citation.volume	57	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	57	en_US

Abstract:

© 2017 Elsevier B.V. In the context of the growth slowdown in China, it is important to accurately forecast the future economic trend to guide policy-makers the direction of adjusting their current economic policies. In this paper, we intend to predict Beijing's tertiary industry, whose datasets are small, irregular and non-stationary, leading to a difficulty of building an accurate prediction model. To this end, we present an improved grey model, named PRGM(1,1), which extends the grey prediction model by integrating two techniques, i.e., the particle swarm optimization algorithm for parameter optimization and the exponential preprocessing method for data cleaning. The experimental results show that PRGM(1,1) outperforms other variants of the grey prediction model in predicting Beijing's tertiary industry, and is viable to do reasonable prediction over short and fluctuated economic data sequences. In addition, we employ PRGM(1,1) in the economic prediction of Beijing's tertiary industry in the next five years, and conclude that the growth rate will decelerate. Our prediction result seems to be in line with the economic slowdown in China this year.

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http://hdl.handle.net/10453/126707