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題名 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
作者 謝淑如
Hsieh, shu ju
貢獻者 周文賢
Chou, wen hsien
謝淑如
Hsieh, shu ju
關鍵詞 轉移函數
時間序列
連續性
季節性
整合
Transfer function
Time series
Consecutive
Seasonal
Integrate
日期 1993
上傳時間 29-四月-2016 16:44:05 (UTC+8)
摘要 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。
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描述 碩士
國立政治大學
統計學系
G80354015
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002004199
資料類型 thesis
dc.contributor.advisor 周文賢zh_TW
dc.contributor.advisor Chou, wen hsienen_US
dc.contributor.author (作者) 謝淑如zh_TW
dc.contributor.author (作者) Hsieh, shu juen_US
dc.creator (作者) 謝淑如zh_TW
dc.creator (作者) Hsieh, shu juen_US
dc.date (日期) 1993en_US
dc.date.accessioned 29-四月-2016 16:44:05 (UTC+8)-
dc.date.available 29-四月-2016 16:44:05 (UTC+8)-
dc.date.issued (上傳時間) 29-四月-2016 16:44:05 (UTC+8)-
dc.identifier (其他 識別碼) B2002004199en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/89024-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) G80354015zh_TW
dc.description.abstract (摘要) 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。		zh_TW
dc.description.tableofcontents 第一章　緒論
      1.1　研究動機‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １
      1.2　研究目的‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １
      1.3　研究方法摘要‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ２
      1.4　章節架構‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ２
     
     第二章　相關文獻探討
      2.1　相關文獻方法回顧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ３
      2.2　模式辨認‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ６
      2.3　參數估計與診斷檢定‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １０
      2.4　模式預測‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １１
     
     第三章　理論架構
      3.1　預測架構建立‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １３
      3.2　ARIMA模式建立‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １３
      3.3　ARIMA轉移函數模式建立‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ３１
      3.4　季節性ARIMA模式建立‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ３８
      3.5　季節性ARIMA轉移函數模式建立‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ４４
      3.6　預測整合‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ４５
     
     第四章　實證分析
      4.1　預測架構及流程‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ５２
      4.2　國民所得預測‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ５３
      4.3　啤酒年銷售量預測‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ５８
      4.4　月均溫預測‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ６３
      4.5　啤酒月銷售量預測‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ６９
      4.6　預測整合‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ７６
     
     第五章　結論
      5.1　研究發現‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ７９
      5.2　應用方向‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ７９
      5.3　研究限制及後續方向‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ８０
     
     參考文獻‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ８１		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002004199en_US
dc.subject (關鍵詞) 轉移函數zh_TW
dc.subject (關鍵詞) 時間序列zh_TW
dc.subject (關鍵詞) 連續性zh_TW
dc.subject (關鍵詞) 季節性zh_TW
dc.subject (關鍵詞) 整合zh_TW
dc.subject (關鍵詞) Transfer functionen_US
dc.subject (關鍵詞) Time seriesen_US
dc.subject (關鍵詞) Consecutiveen_US
dc.subject (關鍵詞) Seasonalen_US
dc.subject (關鍵詞) Integrateen_US
dc.title (題名) 連續性ARIMA轉移函數與季節性ARIMA轉移函數之運用及其整合zh_TW
dc.title (題名) Application and Integration of Consecutive ARIMA Transfer and Seasonal ARIMA Trnasfer Functionen_US
dc.type (資料類型) thesisen_US
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