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Support vector regression (SVR) is a common learning method for machines which is developed these years. Comparing with the other regression models, this method has the advantages of structural risk minimization and strong generalization ability. It is widely used in the prediction field and acquires good effects. The training characters of SVR model are very important to SVR. To solve the problem,...
Wind speed forecasting is very important to the utilization of wind energy in wind farm. In order to improve the forecast precision, a forecasting method based on empirical mode decomposition (EMD) and wavelet decomposition combine with least square support vector machine (LSSVM) is proposed in this paper. The wind speed time series was decomposed into several intrinsic mode functions (IMF) and the...
Due to the fluctuation and complexity of the financial time series, it is difficult to use any single artificial technique to capture its non-stationary property and accurately describe its moving tendency. So a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD) and support vector regression (SVR) is proposed. EMD can adaptively decompose the complicated raw data...
The multi-step forecasting for wind speed is more significant than the single-step one in practice. Aiming to improve the forecasting precision, a novel forecasting approach for wind speed based on empirical mode decomposition (EMD) was presented and its validity was investigated in this paper. The original wind speed sequences were decomposed into some intrinsic mode functions(IMF) in advance employing...
Wind speed forecasting is significant to the security and stability of electric power system. Aiming to forecast wind speed more efficiently, a hybrid forecasting method based on empirical mode decomposition(EMD) and time-series analysis has been presented in this paper. Employing the EMD technique to decompose the original data into a residue and many intrinsic mode function(IMF) components, which...
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