Parameter Selection of SVR Based on Improved K-Fold Cross Validation

Ju E Wang; Jian Zhong Qiao

doi:10.4028/www.scientific.net/AMM.462-463.182

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 462-463Parameter Selection of SVR Based on Improved...

Parameter Selection of SVR Based on Improved K-Fold Cross Validation

Abstract:

This article firstly uses svm to forecast cashmere price time series. The forecasting result mainly depends on parameter selection. The normal parameter selection is based on k-fold cross validation. The k-fold cross validation is suitable for classification. In this essay, k-fold cross validation is improved to ensure that only the older data can be used to forecast latter data to improve prediction accuracy. This essay trains the cashmere price time series data to build mathematical model based on SVM. The selection of the model parameters are based on improved cross validation. The price of Cashmere can be forecasted by the model. The simulation results show that support vector machine has higher fitting precision in the situation of small samples. It is feasible to forecast cashmere price based on SVM.

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Periodical:

Applied Mechanics and Materials (Volumes 462-463)

Pages:

182-186

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.462-463.182

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Cite this paper

Online since:

November 2013

Authors:

Ju E Wang, Jian Zhong Qiao*

Keywords:

Cross Validation, Forecast, Support Vector Machine (SVM), Time Series

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* - Corresponding Author

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