English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62805/95882 (66%)
Visitors : 3982992      Online Users : 533
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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/19850


    Title: Forecasting Telecommunication Traffic Using the Enhanced Stepwise Projection Multiple Regression Method
    Other Titles: 利用逐步投影多元回歸預測電話量
    Authors: Chang, Horng-jinh;Lin, Feng-jenq
    Contributors: 淡江大學經營決策學系
    Keywords: The ESPMR;The base value;Measurement data;Periodical length;Flag
    Date: 1995-01
    Issue Date: 2009-11-30 12:24:23 (UTC+8)
    Publisher: University of Belgrade, Faculty of Organizational Sciences Laboratory for Operations Research
    Abstract: In this paper, one new technological forecasting method, called the Enhanced Stepwise Projection Multiple Regression (ESPMR), is proposed for dealing with the traffic loads that are measurement data but not real values. It combines the adjusting and detecting concepts of SPA (the Sequential Projection Algorithm) with the estimating method of linear regression model, and considers the periodical factor into forecasts if the data series has the periodical traces. Two empirical studies on the forecasting for the International Telecommunication Traffic Loads of Taiwan and the Metropolitan Telecommunication Traffic Loads of Taipei illustrate good and stable results by executing the ESPMR system.
    Relation: Yugoslav Journal of Operations Research 5(2), pp.271-288
    Appears in Collections:[Department of Management Sciences] Journal Article

    Files in This Item:

    File Description SizeFormat
    Forecasting Telecommunication Traffic Using the Enhanced Stepwise Projection Multiple Regression Method.pdf1895KbAdobe PDF25View/Open
    index.html0KbHTML23View/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