Research On Time Series Prediction Based On Wavelet Packet And Least Squares Support Vector Machine

As an important part of nonlinear time series prediction and dynamic complex system,the methods of time series prediction sum up the law and establish prediction model in the change of randomness and uncertainty, so as to provide an effective decision basis for decision-makers. Focusing on the extraction of time series variation feature, the establishment of the prediction model and the optimization of the model parameters, a new method which based on wavelet packet and least squares support vector machines for time series prediction is proposed. This paper is mainly discussed and investigated in such aspects as follows:Firstly, the basic theory of time series prediction method, wavelet analysis and support vector machine is elaborated. Then, the advantages and disadvantages between wavelet transform and wavelet packet transform are analyzed, and the adaptive advantage of wavelet packet in signal feature extraction is discussed. The mechanism of least squares support vector machines is interpreted, and the regression model based on least squares support vector machine is established;Secondly, on the basis of Bayesian Inference, a time series prediction model based on wavelet packet and least squares support vector machines is established. By using wavelet packet decomposition and reconstruction, the time series are decomposed into the approximate sequence and the details sequences, and then reconstructed to the original single level. Separately, the reconstructed sequences are trained and predicted via LSSVM prediction model. By using Bayesian Inference, the model parameters are optimized, and the optimal model is selected to predict. The final prediction result of the original time series is the composition of the respective predictions;Thirdly, According to WP-LSSVM prediction model, and the defect of standard QPSO algorithm, a new method which adjusting the factorβadaptively to optimize the model parameters is proposed. This proposed method makes the search ability of QPSO algorithm more effective, and the convergence faster, so as to improve the prediction accuracy of the results;Fourthly, the proposed method is applied in the Shanghai stock index and the New York Mercantile Exchange crude oil prices respectively, and the experimental results are analyzed and evaluated completely, which shows that the computational speed and prediction accuracy are improved significantly. Above all, the proposed method shows good stability and applicability, which will perform a good prospect.