Research On Chaotic Time Series Prediction Method Based On Support Vector Machines

There is no denying the fact that the method of predicting the future base on historical data is commonly used science, economic and engineering. Time Series Forecasting, which constructs time series model on the basis of historical data and then use the model to forecast the future, is an important research direction in forecasting research area. As far as astronomy, hydrology and meteorological phenomena are concerned, many time series such as sun-spots, amount of runoff, rainfall amount were discovered all including the chaotic character in recent year. In the face of Chaotic Time Series largely existed in nature and social economical phenomena, the traditional method of statistical analysis performed badly. Support Vector Machines possesses excellent non-linear character, which enables it to be extremely suitable to the forecasting research is chaotic array. Based on Support Vector Machines and chaotic theory, the forecasting research has become research hot spot and received special attention at present. This dissertation has done systematic and thorough research on the above mentioned problem.Chaos is a class of random process phenomenon, which appeared in a completely determine dsystem.It is unification of ordered and disorder, deterministic and randomness. Rencently years, it is a new study of non-cycle, complex and irregular phenomenon of method. Chaos prediction of injects new vitality.for theprediction technology.Support Vector Machine (SVM) is based on Statistical Learning Theory (SLT) and Structural Risk Minimization Principle (SRM), and theoretically assures the best model generalization. It changed function estimation to quadratic program, so it can get the optimal solution in theory. Therefore, it is more perfect in theory than Artificial Neural Network (ANN) that is based on Empirical Risk Minimization Principle (ERM).In this paper, SVM is used to establish time series forecasting model, study the parameters that influence forecasting accuracy. On the basis of analyzing model parameters' influence, a self-adaptive optimizing algorithm for establishing the model parameters based on Genetic Algorithm is put forward. Through predicting the sun-spot time series and tapical chaostic time series, it proved that the method has good prediction capability and anti-noise capability.Finally, the method is probed by predicting the cupric and acid time series. the cupric and acid concentrations are single step and multi-step predicted by the method introduced in the dissertation. It is proved that among the methods of selecting the parameters, the one based on Genetic Algorithm greatly improved the predition ability to Chaotic Time Series. It also proved that SVM has better fitting ability and prediction accuracy to Chaotic Time Series.