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Traffic accident forecasting is important for altering and planning of road. Recently time series analysis is an important direction in traffic accident forecasting. Support vector regression (SVR) a kind of SVM used in regression and has better nonlinear forecasting performance than BP neural network. In the paper, the combination method based on particle swarm optimization and support vector regression...
To accurately predict the non-stationary time series, an approach based on integration of wavelet transform, PSO (Particle Swarm Optimization) and SVM (Support Vector Machine) is proposed. Wavelet decomposition is used to reduce the complexity of time series. Different components are predicted by their corresponding SVM forecasters, respectively, after wavelet transform. The final forecasting result...
Economic growth forecasting is important to make the policy on national economic development. Support vector machine (SVM) is a new machine learning method, which seeks to minimize an upper bound of the generalization error instead of the empirical error as in conventional neural networks. In the study, support vector machine and particle swarm optimization is applied in economic growth forecasting,...
The problem of nonlinear time series prediction using integrated intelligent methods based on support vector machine (SVM) and particle swarm optimization (PSO) is studied. Aiming to the open problems of nonlinear time series prediction such as the best number of historical points and parameters of SVM are hard to be determined, a novel model for time series prediction based on PSO and SVM models...
Accurate forecasting of short-term electricity load has been one of the most important issues in the electricity industry. Because of the remarkable nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting electricity load. Support vector machine (SVM) is a novel type of learning machine, which has been successfully employed to solve nonlinear...
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