Application of SVM combined with mackov chain for inventory prediction in supply chain

Wang, J; Sun, D; Zhu, W; Lu, H

Application of SVM combined with mackov chain for inventory prediction in supply chain

Wang, J Sun, D Zhu, W Lu, H

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Publication Type:: Conference Proceeding
Citation:: 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, 2008
Issue Date:: 2008-12-31

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Field	Value	Language
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Sun, D	en_US
dc.contributor.author	Zhu, W	en_US
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.date.issued	2008-12-31	en_US
dc.identifier.citation	2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, 2008	en_US
dc.identifier.isbn	9781424421084	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10957
dc.description.abstract	The aim of this paper is to predict the inventory of the relevant upstream enterprises in supply chain. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish a short-term stage forecasting model. However, take the fact into account that demand signal is affected by variant random factors and behaves big uncertainty, the predicted accuracy of SVM is not approving when the data show great randomness. It is obligatory that we present Markov chain to improve the predicted accuracy of SVM. This combined model takes advantage of the high predictable power of SVM model and at the same time take advantage of the prediction power of Markov chain modeling on the discrete states based on the SVM modeling residual sequence. Then we use the statistical data of the output of the gasoline of China from Feb-06 to Dec-07 for a validation of the effectiveness of the above model. © 2008 IEEE.	en_US
dc.relation.ispartof	2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008	en_US
dc.relation.isbasedon	10.1109/WiCom.2008.1543	en_US
dc.title	Application of SVM combined with mackov chain for inventory prediction in supply chain	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Dalian, China	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

The aim of this paper is to predict the inventory of the relevant upstream enterprises in supply chain. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish a short-term stage forecasting model. However, take the fact into account that demand signal is affected by variant random factors and behaves big uncertainty, the predicted accuracy of SVM is not approving when the data show great randomness. It is obligatory that we present Markov chain to improve the predicted accuracy of SVM. This combined model takes advantage of the high predictable power of SVM model and at the same time take advantage of the prediction power of Markov chain modeling on the discrete states based on the SVM modeling residual sequence. Then we use the statistical data of the output of the gasoline of China from Feb-06 to Dec-07 for a validation of the effectiveness of the above model. © 2008 IEEE.

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http://hdl.handle.net/10453/10957