The Optimization Theory Of Time Series Based On Wavelet Analysis

The optimization method is one of the most important branches of operations research,and it is a very practical subject.It uses computer and mathematics as the main tools for its practical application and theoretical analysis.One of the key problems of the optimization problem is to design an efficient algorithm,which can be divided into linear optimization algorithm and nonlinear optimization algorithm.Nonlinear conjugate gradient method is one of the best methods for solving unconstrained optimization problems.At the same time,the time series analysis is an active branch of mathematical statistics subjects,the application of modern information processing technology research statistics and time series variation of development and characteristics,and predict the future trend of time series.In order to improve the prediction accuracy,it is necessary to better fit the time series model.In this paper,two methods of parameter estimation for time series model are proposed.The conjugate gradient method and the spectral conjugate gradient method are used to estimate the parameters of the time series model.In this paper,we first introduce the relevant knowledge of optimization theory,conjugate gradient method,conjugate gradient method and time series model.Secondly,based on the existing theory,a new conjugate gradient method is proposed,and the parameter estimation problem of time series model is transformed into unconstrained optimization problem.The test results show that the algorithm is effective,and the results show that this method can effectively fit the model parameters.Finally,a new conjugate gradient method is proposed,which is verified by the test function.The numerical results show that the algorithm is effective.