Research on Complicated Time Series Prediction Based on Support Vector Machines

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.