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- W2393368283 abstract "To improve the accuracy of chaotic time series forecasting,Gaussian wavelet support vector machine(SVM)forecasting model is proposed,which combines the wavelet technology with SVM kernel function method,and based on that the wavelet is beneficial to extracting imperceptible features of signal. It is proved that the even order derivative Gaussian wavelet function is an admissible translation-invariant kernel of SVM,and corresponding Gaussian wavelet SVM is constructed. The chaotic time series is reconstructed in phase space,and the vector in phase space reconstruction is used as the input of SVM. The experiments of forecasting Chen's chaotic time series and load chaotic time series are conducted using the proposed SVM,the conventional radial basis SVM and the Morlet wavelet SVM respectively. The comparison results show that Gaussian wavelet SVM has better performance than the other two SVMs." @default.
- W2393368283 created "2016-06-24" @default.
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- W2393368283 date "2009-01-01" @default.
- W2393368283 modified "2023-09-24" @default.
- W2393368283 title "Gaussian Wavelet SVM and Its Applications to Chaotic Time Series Forecasting" @default.
- W2393368283 hasPublicationYear "2009" @default.
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