English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6024/14565 (41%)
Visitors : 13721038      Online Users : 303
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU 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: https://ir.fy.edu.tw:8080/ir/handle/987654321/1090

    Title: Monitoring long-memory air quality data using ARFIMA model
    Authors: Jeh-Nan Pan1;Su-Tsu Chen
    Contributors: 輔英科技大學 共同教育中心 自然科學組
    Keywords: long-memory data;control chart;ARFIMA model;ARIMA model
    Date: 2008-03-01
    Issue Date: 2010-09-24 13:56:56 (UTC+8)
    Abstract: Statistical control chart is commonly used in the industry to help ensure stability of manufacturing process and it can also be used to monitor the environmental data, such as industrial waste or effluent of manufacturing process. However, control chart needs to be modified if the set of environmental data exhibits the property of long memory. In this paper, a control chart for autocorrelated data using autoregressive fractionally integrated moving-average (ARFIMA) model is proposed to monitor the long-memory air quality data. Finally, we use the air quality data of Taiwan as examples to compare the difference between ARFIMA and autoregressive integrated moving-average (ARIMA) models. The results show that residual control charts using ARFIMA models are more appropriate than those using ARIMA models. Copyright © 2007 John Wiley & Sons, Ltd.
    Relation: Environmetrics 19(2),209-219
    Appears in Collections:[自然科學組] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in FYIR are protected by copyright, with all rights reserved.


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