A Novel Hyper-parameters Selection Approach for Support Vector Machines to Predict Time Series
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| Title | A Novel Hyper-parameters Selection Approach for Support Vector Machines to Predict Time Series |
| Authors | |
| Abstract | We propose a novel approach of hyper-parameters selection for SVM regression when it is employed to make time series prediction. In this method, optimal hyper-parameters for SVM are obtained when the residual of training set follows white noise distribution. This conclusion is deduced from the fact that the targets of training set have inherent correlations with each other in time series which is different from other regression problems where the targets of training set are identically and independently distributed. Furthermore, by using this approach, confidence interval can be computed under any given confidence degree which is an important value for many applications. Two algorithms to compute confidence interval are listed in different cases. At last we compare the prediction results on two benchmark time series with cross validation method. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2012-12-01 |
| Source | Journal of Computers Vol 7, No 12 (2012): Special Issue: Advances in Computers and Electronics Engineering |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |