Jisuanji gongcheng · 2005 · 12 citations · 0 references
ComputingData ScienceForecastingMachine LearningPredictive AnalyticsKernel MethodsSupport Vector MachinesTime Series AnalyticsTime Series ForecastingTime Series ModelingTime Series PredictionParameter Sensitivity AnalysisPhase Diagram ConstructionPrediction Model DevelopmentSoft Margin SvmSupport Vector ClassificationSupport Vector Machine ClassificationSupport Vector RegressionSvms
The paper first introduces the technology of phase construction and modeling of time series prediction based on SVM(support vector machines).Then it proposes the multiple-scaled decomposing method of complicated time series and analyzes the parameter sensitivity of SVM regression.Finally,it establishes prediction model and applies it to the stock data.Experimental result indicates that SVM is an effective method for complicated time series prediction.