A Rule-based Forecasting System Integrating combining and Single Forecast for Decision Making

Lin, C-L; Lin, C-C; Z. Shyu, J; Lin, C-T

A Rule-based Forecasting System Integrating combining and Single Forecast for Decision Making

Lin, C-L Lin, C-C Z. Shyu, J Lin, C-T

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Publisher:: ACTA Press
Publication Type:: Journal Article
Citation:: International Journal of Computers and Applications, 2012, 39 (14), pp. 1 - 6
Issue Date:: 2012-02-29

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Field	Value	Language
dc.contributor.author	Lin, C-L	en_US
dc.contributor.author	Lin, C-C	en_US
dc.contributor.author	Z. Shyu, J	en_US
dc.contributor.author	Lin, C-T https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2012-02-29	en_US
dc.identifier.citation	International Journal of Computers and Applications, 2012, 39 (14), pp. 1 - 6	en_US
dc.identifier.issn	1206-212X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115476
dc.description.abstract	Many studies demonstrated that combining forecasts produces consistent but modest gains in accuracy. However, little researches define well the conditions under neither which combining is most effective nor how methods should be combined in each situation. In this paper, a rule-based forecasting system industry is proposed in order to compare forecast performance between combining forecasts and single forecasts, then define these conditions and to specify more effective combinations, finally suggest the best methods. Two comparative case studies for the telecommunications and TFT-LCD industry are proposed to examine the performance of the proposed system. Results from this study indicate that combining forecasts outperform single forecasts only when data set is data have various nonlinear characteristics. In this research, empirical evidence shows that rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals.	en_US
dc.publisher	ACTA Press	en_US
dc.relation.ispartof	International Journal of Computers and Applications	en_US
dc.relation.isbasedon	10.5120/4885-7195	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	A Rule-based Forecasting System Integrating combining and Single Forecast for Decision Making	en_US
dc.type	Journal Article
utslib.citation.volume	14	en_US
utslib.citation.volume	39	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	1702 Cognitive Sciences	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	open_access
pubs.consider-herdc	true	en_US
pubs.issue	14	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

Abstract:

Many studies demonstrated that combining forecasts produces consistent but modest gains in accuracy. However, little researches define well the conditions under neither which combining is most effective nor how methods should be combined in each situation. In this paper, a rule-based forecasting system industry is proposed in order to compare forecast performance between combining forecasts and single forecasts, then define these conditions and to specify more effective combinations, finally suggest the best methods. Two comparative case studies for the telecommunications and TFT-LCD industry are proposed to examine the performance of the proposed system. Results from this study indicate that combining forecasts outperform single forecasts only when data set is data have various nonlinear characteristics. In this research, empirical evidence shows that rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals.

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http://hdl.handle.net/10453/115476