桂林理工大学学报2017，Vol.37Issue(1)：217-222,6.DOI:10.3969/j.issn.1674-9057.2017.01.033
基于小波分析与贝叶斯估计的组合统计建模
Statistical modeling based on the combination of wavelet analysis and Bayesian estimation
摘要
Abstract
The method of wavelet decomposition can decompose time series.First,with the help of the wavelet analysis,the original sequence can decompose term sequence and cycle sequence.The two-part series on ARMA model are established to predict the reconstructed sequence.In order to reduce the estimated cost efficiency,this article uses MCMC method to estimate the parameters of the ARMA model by which term sequence and cycle sequence are set up.However,we can get the regression coefficients and residual term (trend series minus the autoregressive),and use the OLS method to estimate the residual term.Finally,the series are restructured.This paper uses the sample data in forecast and analysis.By railway freight volume data of our country,the empirical analysis results show that introducing wavelet analysis can improve the prediction effect,and the method of combining wavelet analysis with MCMC-OLS estimation has better prediction effect than other methods.关键词
小波分析/贝叶斯估计/ARMA模型/MCMC方法Key words
wavelet analysis/Bayesian estimation/ARMA model/MCMC method分类
数理科学引用本文复制引用
林静,唐国强,覃良文..基于小波分析与贝叶斯估计的组合统计建模[J].桂林理工大学学报,2017,37(1):217-222,6.基金项目
国家自然科学基金项目(41101136) （41101136）
国家社会科学基金项目(13CJY075) （13CJY075）
广西财经学院数量经济学重点实验室项目(2014) （2014）
广西空间信息与测绘重点实验室项目(15-140-07-33) （15-140-07-33）
