微型电脑应用2018，Vol.34Issue(3)：38-41,46,5.
平稳时序数据的Bootstrap辨识及其改进算法研究
Bootstrap identification of stationary time series data and its improved algorithm
摘要
Abstract
In the process of identification of auto-regressive stationary time series data,by introducing Bootstrap method,to resampling,parameter estimation and model updating of identification residual sequence the identification accuracy can be imepoved for the original model.The following two improvements on the existing Bootstrap method are preseated based on the auto-regressive identification algorithm.One is to use auto-regressive model as sliding window width,toprocess the residuals resampling processing;The another is based on iterative matrix singular value decomposition theory to sdve the parameters of the Bootstrap sequence for re-sampling.Experimental results show that the proposed algorithm can effectively improve the identification accuracy and speed of the existing algorithms,and improve the availability of the existing algorithms.关键词
平稳时序数据/Bootstrap方法/自回归模型/重抽样Key words
stationary time series data/Bootstrap method/auto-regressive model/re-sampling分类
信息技术与安全科学引用本文复制引用
黄雄波..平稳时序数据的Bootstrap辨识及其改进算法研究[J].微型电脑应用,2018,34(3):38-41,46,5.基金项目
广东省科技计划工业攻关项目(2011B010200031) （2011B010200031）
佛山职业技术学院校级重点科研项目(2015KY006) （2015KY006）
