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This paper deals with modelling high-volatile time series using modern machine learning technique called Support Vector Regression. After discussing the basic principles of Support Vector Machines (SVM), we construct SVM Regression Prediction Model. Afterwards, this prediction SVR model is applied to oil prices. Due to high-volatile and dynamic character these data are very difficult to model. Experiments...
In this paper, the car sales prediction model is established by using Support Vector Regression (SVR) combined with Particles Swarm Optimization algorithm (PSO-SVR). In this model, PSO Algorithm is used to optimize the 3 parameter used in Support Vector Regression. PSO algorithm not only has a strong global search capability, but also solved the problem of over-fitting. Moreover, Mean Absolute Percentage...
Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Hourly cooling load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day. So the accuracy of forecasting is influenced by many unpredicted factors...
In order to ensure safety in coal production, full assurance is given for fully-mechanized excavated faces. Based on the vector supporting machine for regression (SVR), a model is established for predicting the gas emission in fully-mechanized excavated faces. The index system is analyzed and the model parameters are chosen. Then, the sample set of gas emission in fully-mechanized coal driving workface...
The support vector machine (SVM), proposed by Vapnik (1995), has been successfully applied to classification, cluster, and forecast. This study proposes support vector regression (SVR) to forecast real estate prices in China. The aim of this paper is to examine the feasibility of SVR in real estate price prediction. To achieve the aim, five indicators are selected as the input variables and real estate...
The advantages and disadvantages of grey forecast method are analyzed respectively. The grey error forecast method based on support vector regression (SVR) is proposed in this article. The new method remedy the disadvantages of grey forecast model and weakens the stochastic undulation, avoids the theoretical defects existing in the grey forecast model. The forecast effect is improved for non-linear...
SVM which is based on statistical theory has the advantage of no relying on designer's experience of learning and the prior knowledge. So it is widely used in optimization, decision-making, regression estimates, speech recognition, facial image recognition, and so on. Because there are some kinds of wrong and isolated samples in the training samples in the forecasting model, and the learning process...
As a learning mechanic, support vector machine (SVMs) has been studied and applied in a wide area. This study deals with the special futures of SVM in predicting the total workload in telecommunication. The contributions include: (a) Building a predicted model of the total workload in telecommunications and predicting using it; (b)Analyzing the parameter of support vector regression(SVRs) which influence...
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