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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/19545

    题名: A methodology for selecting subset autoregressive time series models
    作者: Yu, Gwo-hsing;Lin, Yow-chang
    贡献者: 淡江大學水資源及環境工程學系
    关键词: Subset autoregressive model;inverse autocorrelation function;Bhansali information criterion
    日期: 1991-07
    上传时间: 2009-11-04 17:06:34 (UTC+8)
    出版者: Blackwell
    摘要: 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.
    關聯: Journal of time series analysis 12(4), pp.363-373
    DOI: 10.1111/j.1467-9892.1991.tb00090.x
    显示于类别:[水資源及環境工程學系暨研究所] 期刊論文


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