English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49195/83607 (59%)
Visitors : 7092521      Online Users : 41
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/19545


    Title: A methodology for selecting subset autoregressive time series models
    Authors: 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: 2009-11-04 17:06:34 (UTC+8)
    Publisher: Blackwell
    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), pp.363-373
    DOI: 10.1111/j.1467-9892.1991.tb00090.x
    Appears in Collections:[水資源及環境工程學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    0143-9782_12(4)p363-373.pdf473KbAdobe PDF318View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback