Methodology for selecting subset autoregressive time series models

doi:10.1111/j.1467-9892.1991.tb00090.x

機構典藏 > College of Engineering > Graduate Institute & Department of Water Resources and Environmental Engineering > Journal Article > Item 987654321/67854

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/67854

Title:	Methodology for selecting subset autoregressive time series models
Authors:	虞國興;Yu, Gwo-hsing;Lin, Yow-chang
Contributors:	淡江大學水資源及環境工程學系
Keywords:	Subset autoregressive model;inverse autocorrelation function;Bhansali information criterion
Date:	1991-07
Issue Date:	2011-10-23 02:08:18 (UTC+8)
Abstract:	In time series modelling, subset models are often desirable, especially when the data exhibit some form of periodic behaviour with a range of different natural periods in terms of days, weeks, months and years. Recently, Hokstad proposed a method based on personal judgement for selecting the first tentative model to obtain the best subset autoregressive model. The subjective approach adopted in the Hokstad method is a disadvantage in building up a computer program which could automatically select the appropriate model of a given time series. In this paper, we propose overcoming this disadvantage by employing the inverse autocorrelation function to select the first tentative model. In addition to sets of synthetic data, some well-known real series such as the D, E and F series of Box and Jenkins and the Canadian lynx data are analysed to validate the proposed method. The results indicate that the method can successfully detect the true model for a given time series.
Relation:	Journal of time series analysis 12(4), p.363-373
DOI:	10.1111/j.1467-9892.1991.tb00090.x
Appears in Collections:	[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

Files in This Item:

File	Size	Format
Methodology for selecting subset autoregressive time series models.html	0Kb	HTML	24	View/Open

Loading...