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題名 時空數列分析在蔬菜價格變動與產銷策略之研究
Spatial Time Series Analysis and it`s Application : A Production- Marketing Strategy for the Vegetables Price
作者 譚光榮
Tan, kuang Jung
貢獻者 吳柏林
Wu, Berlin
譚光榮
Tan, kuang Jung
關鍵詞 時空數列
STARMA模式
自相關
預測
Weighting matrix
STARMA model
Autocorrelation
Forecast
日期 1993
上傳時間 29-Apr-2016 16:43:47 (UTC+8)
摘要 蔬菜的供給彈性非常小，收成之後，不僅產量會決定售價的高低，同類蔬菜之間的替代效果，對於價格變化也有很大的影響力。因此若能事先預測同類蔬菜未來的價格變化，即可計劃各類蔬菜的生產量。在本篇論文中，我們試著將時空數列應用在非空間系統的經濟領域上。以臺灣地區三種常見的蔬菜為例，分別以時空數列的 STARMA 模式與單變量 ARIMA 時間數列，利用蔬菜批發價格建立模式，並比較其短期預測效果。最後，就價格變動與產銷策略之關係進行討論。
The supply elasticity of vegetables is so small. Once the production has been known, it would reflect on the price as soon as possible. And at the same time, the substitute effect between the vegetables has also great influence on the change of the price. However, if we could forecast the variation of the vegetables price,then the production-marketing strategy would be planned in advance. In this paper, we apply the spatial time series analysis on the field of economic, which is included in the non-spatial system. An investigate about the price variation for three kinds of vegetables in Taiwan.And the comparison of short-term forecasting performance for the STARMA model and traditional ARIMA model are also made. Finally, we discuss in detail about the relationship between the change of vegetable price and production-marketing strategy.
參考文獻 Ali, M.M.(1979). Analysis of stationary spatial temporal processes: estimation
     and prediction,Biometrika,66,513-518.
     Bennett, R.J. (1979). Spatial time series,Pion Lin1ited,London.
     Bennett, R.J.(1984). Advances in the analysis of spatial time series ,Spatial
     Statistics and lvlodels,235-251.
     Besag ,J. (1974). Spatial interaction and the statistical analysis of lattice
     systen1s, Journal of the Royal Statistical Society)B, 36,192-225.
     Box, G.E.P. and Jenkins, G.Iv1.(1976). Time series analysis forecasting and
     control,2nd ed.,Rolden-Day,San-Francisco.
     Cliff, A.D. and Ord, J.K.(1981). Spatial processes: models and applications,
     Pion Limited,London.
     Deutsch ,S.J. and Pfeifer, P.E.(1980a). A Three-Stage iterative procedure for
     space-tilDe modeling , Techno`metrics,22 ,35-47.
     Deutsch ,S.J. and Pfeifer, P.E. (1980b). Identification and interpretation of
     first order space-tilDe ARIv1A models 1 Technometrics, 22,397-408.
     Deutsch ,S.J. and Pfeifer, P.E.(1981). Space-Time ARIv1A modeling with
     contemporaneously correlated innovations, Techno`metrics,23, 401-409.
     Flahault ,A.,et al. (1988). IvIodelling the 1985 influenza epidemic in Frence ,
     Statistics in Medicine,7,1147-1155.
     Griffith, D.A., Raining, R.P. and Bennett, R.J. (1985). Estimating missing
     values in space-time data series, Time Series Analysis: Theory and
     Practice 6,273-296.
     Haslett, J. and Raftery, A.E.(1989). Space-Tilne modelling with long - memory
     dependence : assessing Ireland`s wind power resource , Applied
     Statistics,38,l-50.
     NIann,H.B. and vVald,A.(1943). On the statistical treatment of linear stochastic
     dfference equations, Econometrika, II, 173-270.
     Pfeifer, P.E. and Bodily, S.E. (1990). A Test of space-tinle ARNIA modelling
     and forcasting of hotel data, J o`Uxnal of Forecasting ,9, 255-272.
     Raftery, A.E.,Haslett, J. and NIcColI, E.(1982). ,;Vind power: A space-time
     process, Time Series Analysis: Theory and Practice 2, 191-202.
     Rogers, A.(1974). Statistical analysis of spatial dispersion: the quadrat
     method, Pion Limited,London.
     Stoffer, D.S.(1986). Estimation and identification of space-time ARNIAX
     models in the presence of n1issing data , Journal of the American Statistical
     Association, 81,762-772.
     Tjostheim, D.(1978). Statistical spatial series modelling, Adv. Appl. Frob.
     ,10, 130-154.
     Wei, Willian1 W.S.(1990). Tims series analysis: univariate and multivariate
     methods, Addison-Wesley.
     Zeger, S.L.(1985). Exploring an ozone spatial time series in the frequency
     dOlnain ,Jo`urnal of the American Statistical Associations, 80, 323-33l.
描述 碩士
國立政治大學
統計學系
G80354004
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002004191
資料類型 thesis
dc.contributor.advisor 吳柏林zh_TW
dc.contributor.advisor Wu, Berlinen_US
dc.contributor.author (Authors) 譚光榮zh_TW
dc.contributor.author (Authors) Tan, kuang Jungen_US
dc.creator (作者) 譚光榮zh_TW
dc.creator (作者) Tan, kuang Jungen_US
dc.date (日期) 1993en_US
dc.date.accessioned 29-Apr-2016 16:43:47 (UTC+8)-
dc.date.available 29-Apr-2016 16:43:47 (UTC+8)-
dc.date.issued (上傳時間) 29-Apr-2016 16:43:47 (UTC+8)-
dc.identifier (Other Identifiers) B2002004191en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/89016-
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) G80354004zh_TW
dc.description.abstract (摘要) 蔬菜的供給彈性非常小，收成之後，不僅產量會決定售價的高低，同類蔬菜之間的替代效果，對於價格變化也有很大的影響力。因此若能事先預測同類蔬菜未來的價格變化，即可計劃各類蔬菜的生產量。在本篇論文中，我們試著將時空數列應用在非空間系統的經濟領域上。以臺灣地區三種常見的蔬菜為例，分別以時空數列的 STARMA 模式與單變量 ARIMA 時間數列，利用蔬菜批發價格建立模式，並比較其短期預測效果。最後，就價格變動與產銷策略之關係進行討論。zh_TW
dc.description.abstract (摘要) The supply elasticity of vegetables is so small. Once the production has been known, it would reflect on the price as soon as possible. And at the same time, the substitute effect between the vegetables has also great influence on the change of the price. However, if we could forecast the variation of the vegetables price,then the production-marketing strategy would be planned in advance. In this paper, we apply the spatial time series analysis on the field of economic, which is included in the non-spatial system. An investigate about the price variation for three kinds of vegetables in Taiwan.And the comparison of short-term forecasting performance for the STARMA model and traditional ARIMA model are also made. Finally, we discuss in detail about the relationship between the change of vegetable price and production-marketing strategy.en_US
dc.description.tableofcontents 第一章　前言‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　１
     
     第二章　時空數列模式
     　第一節　STARMA模式‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　３
     　第二節　空間階數與加權矩陣‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　５
     
     第三章　建立時空數列模式
     　第一節　確認模式‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　１１
     　第二節　估計參數‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　１４
     　第三節　模式診斷及檢驗‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　１６
     
     第四章　時空數列在產銷策略之研究
     　第一節　實例研究‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　１８
     　第二節　結果比較與分析‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　２４
     　第三節　蔬菜價格與產銷策略‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　２９
     
     第五章　結論‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　３１
     
     參考文獻　‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧　３３		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002004191en_US
dc.subject (關鍵詞) 時空數列zh_TW
dc.subject (關鍵詞) STARMA模式zh_TW
dc.subject (關鍵詞) 自相關zh_TW
dc.subject (關鍵詞) 預測zh_TW
dc.subject (關鍵詞) Weighting matrixen_US
dc.subject (關鍵詞) STARMA modelen_US
dc.subject (關鍵詞) Autocorrelationen_US
dc.subject (關鍵詞) Forecasten_US
dc.title (題名) 時空數列分析在蔬菜價格變動與產銷策略之研究zh_TW
dc.title (題名) Spatial Time Series Analysis and it`s Application : A Production- Marketing Strategy for the Vegetables Priceen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Ali, M.M.(1979). Analysis of stationary spatial temporal processes: estimation
     and prediction,Biometrika,66,513-518.
     Bennett, R.J. (1979). Spatial time series,Pion Lin1ited,London.
     Bennett, R.J.(1984). Advances in the analysis of spatial time series ,Spatial
     Statistics and lvlodels,235-251.
     Besag ,J. (1974). Spatial interaction and the statistical analysis of lattice
     systen1s, Journal of the Royal Statistical Society)B, 36,192-225.
     Box, G.E.P. and Jenkins, G.Iv1.(1976). Time series analysis forecasting and
     control,2nd ed.,Rolden-Day,San-Francisco.
     Cliff, A.D. and Ord, J.K.(1981). Spatial processes: models and applications,
     Pion Limited,London.
     Deutsch ,S.J. and Pfeifer, P.E.(1980a). A Three-Stage iterative procedure for
     space-tilDe modeling , Techno`metrics,22 ,35-47.
     Deutsch ,S.J. and Pfeifer, P.E. (1980b). Identification and interpretation of
     first order space-tilDe ARIv1A models 1 Technometrics, 22,397-408.
     Deutsch ,S.J. and Pfeifer, P.E.(1981). Space-Time ARIv1A modeling with
     contemporaneously correlated innovations, Techno`metrics,23, 401-409.
     Flahault ,A.,et al. (1988). IvIodelling the 1985 influenza epidemic in Frence ,
     Statistics in Medicine,7,1147-1155.
     Griffith, D.A., Raining, R.P. and Bennett, R.J. (1985). Estimating missing
     values in space-time data series, Time Series Analysis: Theory and
     Practice 6,273-296.
     Haslett, J. and Raftery, A.E.(1989). Space-Tilne modelling with long - memory
     dependence : assessing Ireland`s wind power resource , Applied
     Statistics,38,l-50.
     NIann,H.B. and vVald,A.(1943). On the statistical treatment of linear stochastic
     dfference equations, Econometrika, II, 173-270.
     Pfeifer, P.E. and Bodily, S.E. (1990). A Test of space-tinle ARNIA modelling
     and forcasting of hotel data, J o`Uxnal of Forecasting ,9, 255-272.
     Raftery, A.E.,Haslett, J. and NIcColI, E.(1982). ,;Vind power: A space-time
     process, Time Series Analysis: Theory and Practice 2, 191-202.
     Rogers, A.(1974). Statistical analysis of spatial dispersion: the quadrat
     method, Pion Limited,London.
     Stoffer, D.S.(1986). Estimation and identification of space-time ARNIAX
     models in the presence of n1issing data , Journal of the American Statistical
     Association, 81,762-772.
     Tjostheim, D.(1978). Statistical spatial series modelling, Adv. Appl. Frob.
     ,10, 130-154.
     Wei, Willian1 W.S.(1990). Tims series analysis: univariate and multivariate
     methods, Addison-Wesley.
     Zeger, S.L.(1985). Exploring an ozone spatial time series in the frequency
     dOlnain ,Jo`urnal of the American Statistical Associations, 80, 323-33l.		zh_TW