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Support vector machines (SVM) is a widely used method which can treat problems involving small sample, devilish learning, and high dimension. The current paper conduct a multivariate SVM in a total-factor production framework, and the GDP per capita, capital stock and labor are taken as the independent variables and the energy consumption is the dependent variable. The Gaussian radial basis function...
In this study, detection of small target in chaotic clutter with unknown dynamics is presented. We achieve this in four steps: (i) by using db3 wavelet decomposition of the signals, (ii) using Takens delay embedding theorem and least-squares support vector machine (LS-SVM) prediction, including increase the symmetric constraint and improve the kernel function, (iii) wavelet reconstruction, (iv) separation...
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 forecast of air passenger flow plays an important role in the management of airline, but the traditional forecast methods can't guarantee the generalization capability when they face a large-scale, multi-dimension, nonlinear and non-normal distribution time series data. To improve the forecast ability of air passenger flow, the SVM regression algorithm is introduced in this paper. By selecting...
In blast furnace (BF) ironmaking process, silicon content in hot metal is an important index, which reflects the thermal state of BF. To predict the silicon content in hot metal effectively and level up the forecasting accuracy, a novel combined model based on empirical mode decomposition (EMD) and support vector machine (SVM) is proposed. Firstly, the time series data of silicon content in hot metal...
The technical analysis of financial time series and in particular the prediction of future developments is a challenging problem that has been addressed by many researchers and practitioners due to the possible profit. We provide a forecasting technique based on a certain machine learning paradigm, namely support vector machines (SVM). SVM gained more and more importance for practical applications...
According to the noise in the nonlinear systems and shortage of chaotic prediction method at present, this paper presents a local linear adaptive prediction algorithm based on the kernel function of wavelet decomposition. This method using wavelet transformation has a unique multi-scale analysis capability, decomposed the singular into low frequency part and high frequency part, thereby it can reduce...
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