NDLI: A ovel Parameters Selection Approach for Support Vector Machines to Predict Time Series

Content Provider	IEEE Xplore Digital Library
Author	Yanhua Yu Zhijun Ren
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Beijing Univ. of Posts & Telecommun., Beijing (Yanhua Yu; Zhijun Ren)
Abstract	Aimed to solve the problem that there is no structural approach to select the best free parameters for Support Vector Machines when being used in time series prediction, a novel approach is proposed. In this method, best parameters for SVM is not got at the minimal MSE (Mean Squared Error) of validation set, but that the residue of training set is in White Noise form. This conclusion is deduced from the fact that the targets of training set have inherent correlations with each other. This approach is also effective to predict time series with nolinear and non-stationary characteristics. Furthermore, by using this method, confidence interval can be computed under any given confidence degree $^{1}$ $^{-}$ $^{alpha}$ which is an important value for many applications. Two algorithms to compute confidence interval are given under different circumstances. Program is also given about how to make dynamic on-line prediction. Experiment was made to predict the annual sunspot number and perfect result was achieved.
Starting Page	845
Ending Page	850
File Size	1330801
Page Count	6
File Format	PDF
ISBN	9781424420209
DOI	10.1109/ICPCA.2008.4783728
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-06
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Random access memory Predictive models Educational institutions time series Pattern recognition Support Vector Machines Lagrangian functions SRM Support vector machines Computer science white noise Support vector machine classification White noise Risk management
Content Type	Text
Resource Type	Article

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