Nonlinear time series prediction is studied by using an improved least squares support vector machine （LSSVM） regression based on chaotic mutation evolutionary programming （CMEP） approach for parameter optimizatio...Nonlinear time series prediction is studied by using an improved least squares support vector machine （LSSVM） regression based on chaotic mutation evolutionary programming （CMEP） approach for parameter optimization. We analyze how the prediction error varies with different parameters （σ, γ） in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.展开更多
基金The project supported by National Natural Science Foundation of China under Grant No. 90203008 and the Doctoral Foundation of the Ministry of Education of China
文摘Nonlinear time series prediction is studied by using an improved least squares support vector machine （LSSVM） regression based on chaotic mutation evolutionary programming （CMEP） approach for parameter optimization. We analyze how the prediction error varies with different parameters （σ, γ） in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.
