Break detection in nonstationary strongly dependent long time series

Author

Song, Li ; Bondon, Pascal

Author_Institution

Univ. Paris-Sud, Gif-sur-Yvette, France

fYear

2011

fDate

28-30 June 2011

Firstpage

577

Lastpage

580

Abstract

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.

Keywords

Monte Carlo methods; autoregressive moving average processes; signal processing; time series; Monte Carlo simulation; break detection; parameter estimation; piecewise fractional autoregressive integrated moving average model; real traffic data modelling; signal series; structural break point; time series; Computational modeling; Correlation; Data models; Estimation; Monte Carlo methods; Numerical models; Time series analysis; Long time series; Piecewise model; Strongly dependent; Structural breaks;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing Workshop (SSP), 2011 IEEE

Conference_Location

Nice

ISSN

pending

Print_ISBN

978-1-4577-0569-4

Type

conf

DOI

10.1109/SSP.2011.5967763

Filename

5967763