Journal
COMPUTATIONAL MANAGEMENT SCIENCE
Volume 3, Issue 2, Pages 113-129Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10287-005-0003-7
Keywords
Least squares support vector machines; Nystrom approximation; Fixed-size LS-SVM; Kernel based methods; Sparseness; Primal space regression; Load forecasting; Time series
Categories
Funding
- Flemish Government (FWO) [G.0211.05, G.0240.99, G.0407.02, G.0197.02, G.0141.03, G.0491.03, G.0120.03, G.0452.04, G.0499.04]
- Flemish Government (ICCoS, ANMMM)
- Flemish Government (AWI)
- Flemish Government (IWT)
- Flemish Government (GBOU (McKnow) Soft4s)
- Belgian Federal Government (Belgian Federal Science Policy Office) [IUAP V-22]
- Belgian Federal Government (PODO-II) [CP/01/40]
- EU (FP5-Quprodis
- ERNSI) [Eureka 2063-Impact, Eureka 2419-FLiTE]
- ISMC /IPCOS
- Data4s
- TML
- Elia
- LMS
- IPCOS
- Mastercard
- [GOA-Mefisto 666]
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Based on the Nystrom approximation and the primal-dual formulation of the least squares support vector machines, it becomes possible to apply a nonlinear model to a large scale regression problem. This is done by using a sparse approximation of the nonlinear mapping induced by the kernel matrix, with an active selection of support vectors based on quadratic Renyi entropy criteria. The methodology is applied to the case of load forecasting as an example of a real-life large scale problem in industry. The forecasting performance, over ten different load series, shows satisfactory results when the sparse representation is built with less than 3% of the available sample.
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