NDLI: SMO Algorithm Applied in Time Series Model Building and Forecast

Content Provider	IEEE Xplore Digital Library
Author	Jin-Fang Yang Yong-Jie Zhai Da-Ping Xu Pu Han
Copyright Year	2007
Description	Author affiliation: North China Electr. Power Univ., Baoding (Jin-Fang Yang; Yong-Jie Zhai; Da-Ping Xu; Pu Han)
Abstract	As a novel learning machine, the support vector machine (SVM) based on statistical learning theory can be used for regression: support vector regression (SVR). SVR has been applied successfully to time-series analysis, but its optimization algorithm is usually built up from certain quadratic programming (QP) packages. Therefore, for small datasets this is practical and QP routines are the best choice, but for large datasets, data processing runtimes become lengthy, which limits its application. Sequential minimal optimization (SMO) algorithm can improve operation speed and reduce this long runtime. In this paper, SVR that is based on the SMO algorithm is used to forecast two typical time series models: Wolfer sunspot number data and Box and Jenkins gas furnace data. The results of simulation prove that the operational speed of SVR using the SMO algorithm is improved in comparison to SVR employing QP optimization algorithm; moreover, the forecasting precision is better than that of neural network and SVR using QP optimization algorithm.
Starting Page	2395
Ending Page	2400
File Size	582285
Page Count	6
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370546
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Quadratic programming Support vector machines Runtime Machine learning Statistical learning Time series analysis Algorithm design and analysis Packaging machines Data processing Support vector machine Model analysis and forecast Time series Sequential minimal optimization (SMO) algorithm Support vector regression
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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