BREAK DETECTION IN NONSTATIONARY STRONGLY DEPENDENT LONG TIME SERIES

被引：0
|
作者：
Song, Li [1 ]
Bondon, Pascal [1 ]
机构：
[1] Univ Paris 11, CNRS, UMR 8506, F-91192 Gif Sur Yvette, France
来源：
2011 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2011年
关键词：
Piecewise model; Structural breaks; Strongly dependent; Long time series; SELF-SIMILARITY;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We consider the problem of fitting a piecewise fractional autoregressive integrated moving average model to strongly dependent signals with large data. The number as well as the locations of structural break points, the model order and the parameters of each regime are assumed to be unknown. A four-step method based on distances between parameter estimates is proposed, to avoid the optimization problem which criterion based methods may be trapped in when there are a lot of data in the signal series. Monte Carlo simulations show the effectiveness of the method with different distances and an application to real traffic data modelling is considered.
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页码：577 / 580
页数：4
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