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Least Squares Support Vector Machine (LS-SVM) is a classic algorithm for regression estimation and classification. But unfortunately, for really large problems, LS-SVM can become highly memory and time consuming. In this paper, we present a simplified algorithm for LS-SVM, called ILS-SVM, which effectively reduces the algorithmic complexity. In order to improve the rate of convergence and overcome...
It is important to choose good parameters in support vector regression (SVR) modeling. Choosing different parameters will influence the accuracy of SVR models. This paper proposes a parameter choosing method of SVR models for time series prediction. In the light of data features of time series, the paper improves the traditional cross-validation method, and combines the improved cross-validation with...
Recently, Epsilon-Insensitive Support Vector Regression (epsiv SVR) has been introduced to solve regression and prediction problems. However, the preprocessing of data set and the selection of parameters can become a real computational burden to developer and user. Improper parameters usually lead to prediction performance degradation. In this paper, by introducing Parallel Multidimensional Step Search...
The application of SVR in the time series prediction is increasingly popular. Because some time series prediction based on SVR wasn 't very nice in the efficiency of the forecast, this article presents a new regression based on linear regression and SVR. The new regression separates time series into linear part and nonlinear part, then predicts the two parts respectively, and finally integrates the...
Recently, support vector machine (SVM) as a new kernel learning algorithm has successfully been used in nonlinear time series prediction. To improve the prediction performance of SVM, We concentrate on ensemble method. Bagging and boosting, two famous ensemble methods, are examined in this paper. Experiments on two data sets (sunspots and Mackey-Glass) have shown that bagging SVR and boosting SVR...
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