GA-SVM based framework for time series forecasting

Nguyen Thi, Gordon Brown Lee, Peter Wheeler, Jim Peterson

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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A framework (hereby named GA-SVM) for time series forecasting was formed by integration of the particular power of Genetic Algorithms (GAs) with the modeling power of the Support Vector Machine (SVM). The proposed system has potential to capture the benefits of both fascinating fields into a single framework. GAs offer high capability in choosing inputs that are relevant and necessary in predicting dependent variables. With these selected inputs, SVM becomes more accurate in modeling the estimation problems. Experiments demonstrated that the integrated GA-SVM approach is superior compared to conventional SVM applications.

Original languageEnglish
Title of host publication5th International Conference on Natural Computation, ICNC 2009
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Print)9780769537368
Publication statusPublished - 2009
EventInternational Conference on Natural Computation 2009 - Tianjian, China
Duration: 14 Aug 200916 Aug 2009
Conference number: 5th (Proceedings)


ConferenceInternational Conference on Natural Computation 2009
Abbreviated titleICNC 2009
Internet address

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