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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/89024
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/89024


    Title: 連續性ARIMA轉移函數與季節性ARIMA轉移函數之運用及其整合
    Application and Integration of Consecutive ARIMA Transfer and Seasonal ARIMA Trnasfer Function
    Authors: 謝淑如
    Hsieh, shu ju
    Contributors: 周文賢
    Chou, wen hsien
    謝淑如
    Hsieh, shu ju
    Keywords: 轉移函數
    時間序列
    連續性
    季節性
    整合
    Transfer function
    Time series
    Consecutive
    Seasonal
    Integrate
    Date: 1993
    Issue Date: 2016-04-29 16:44:05 (UTC+8)
    Abstract: 為因應預測目的不同,有時需要各種預測水平{\\rm (forecast horizon)}
    ,例如,月預測可供進料、生產、補貨及倉儲之參考,年預測則可作為產
    能規畫、產品線規畫、投資決策等之準則。然而,預測結構卻會因水平的
    不同而彼此相異,以致產生諸多預測值的矛盾。有鑑於此,本研究主要以
    一簡單且具理論基礎的整合{\\rm intergration)} 過程,解決預測值互相
    矛盾的問題。由於年資料通常屬於連續性模式,月資料則多為季節性模式
    ,兩者透過的轉移函數形態截然不同,而且在解釋變數的選取上更是迥異
    ,因此,需要經由加權平均的整合,才能使月預測值的加總等於年預測值
    。至於權數的決定則以離散程度為準則,由於年資料為月資料的加總,兩
    者均值相差甚多,故以變異係數為測量離散情形的標準。本研究主要乃遵
    循{\\rm Box-Jenkins} 的模式建立法則,構建連續性轉移函數模式及季節
    性、轉移函數模式,並加以整合調整。在實證分析中以台灣啤酒銷售量為
    例說明預測流程,年銷量預測方面以國民所得為解釋變數; 月銷量預測方
    面則以氣溫為解釋變數,最後以加權平均將兩者整合調整。
    Reference: (1). 魏庭、澤(1982), 數理統計學
    (2). 徐瑞玲(1988),『時間數列模型建立各種分析方法的比較與實證研
    究』,政治大學統計研究所
    (3). 顏月珠(1990), 商用統計學y 台北:三民。
    (4). 周文賢及游文清(1990), 計量經濟與時間序列分析/SASETS之運
    用, 台北, 教育部電算中心。
    (5). 陳儀庭(1992), 『進出口貿易預測系統之建立運用-以個案為例』,
    政治大學統計研究所


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    Description: 碩士
    國立政治大學
    統計學系
    G80354015
    Source URI: http://thesis.lib.nccu.edu.tw/record/#B2002004199
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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