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題名 信心度函數與模糊時間數列預測
Belief Function and Fuzzy Time Series Forecasting作者 楊勝斌 貢獻者 吳柏林
楊勝斌關鍵詞 信心度函數
模糊時間數列預測
模糊規則庫
平均預測秩階準確度日期 2002 上傳時間 9-May-2016 16:39:22 (UTC+8) 摘要 投資的獲利多寡並不單單基於預測的準確性，信心度的大小亦攸關獲利的結果。因為信心度愈大，則投資人將會提高投資的金額，而獲得更多的利潤。反之，雖然預測的結果是準確的，但若信心度很小，則投資人將不敢投入較多的金額，如此一來所獲得的利潤就有限了。本文嘗試著應用信心度函數來輔助說明多變量模糊時間數列預測結果，亦即預測模式對預測結果的屬性所具有的信心程度。最後利用多變量模糊時間數列模式，結合加權股價指數的收盤價及成交量兩個變量，針對台灣加權股價指數進行預測及衡量預測屬性的信心度。相信這對於風險控管及提高投資報酬深具意義。
謝辭 摘要 目錄-----1 1. 前言-----2 2. 模糊時間數列分析與預測-----4 2.1 模糊邏輯之引進-----4 2.2 模糊時間數列分析-----5 2.3 如何由模糊規則庫進行屬性判別-----8 2.4 多變量模糊時間數列的預測-----11 2.5 平均預測秩階準確度-----12 3. 信心度函數-----13 3.1 信心函數-----13 3.2 如何建構與計算信心度-----15 4. 實證分析與結果-----19 4.1 資料分析-----19 4.2 模糊時間數列模式建構-----21 4.3 預測結果的比較與分析-----25 5. 結論-----28 參考文獻-----30參考文獻 Chen, S. M. (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81, 311-319. Chiang, D., Chow, L., and Wang Y.(2000) Mining time series data by a fuzzy linguistic summary system. Fuzzy Sets and Systems, 112, 419-432. Graham, B. P. and Newell, R. B. (1989) Fuzzy adaptive control of a first-order process. Fuzzy Sets and Systems, 31, 47-65. Hendershot, G. and Placek, P. (Eds) (1981) Predicting Fertility. Lexington, MA: health and Co. K. Huarng（2001）Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems,123,369-386. Klir, G. F. and Folger, T. A. (1988) Fuzzy Sets, Uncertainly and Information. Englewood Cliffs, NJ:Prentice Hall. Lee, Y. C., Hwnag, C. and Shih, Y. P. (1994) A Combined Approach to Fuzzy Model Identification. IEEE Transactions on Systems, Man, and Cybernetics, Vol.24, No.5, 736-743. Manski, C. (1990) The Use of Intention Data to Predict Behavior: A Best Case Analysis. Journal of the American Statistical Association, 85, 934-940. Song, Q. and Chissom, B. S. (1993a) Forecasting enrollments with fuzzy time series─Part I. Fuzzy Sets and Systems, 54, 1-9. Song, Q. and Chissom, B. S. (1993b) Fuzzy time series and its models. Fuzzy Sets and Systems, 54, 269-277. Song, Q. and Chissom, B. S. (1994) Forecasting enrollments with fuzzy time series─Part II. Fuzzy Sets and Systems, 62, 1-8. Song, Q., Leland, R. P. and Chissom, B. S. (1997) Fuzzy stochastic fuzzy time series and its models. Fuzzy Sets and Systems, 88, 333-341. Tong, R. M. (1978) Synthesis of fuzzy models for industrial processes. Int. J. Gen. Syst., Vol.4, 143-162. Tseng, F., Tzeng, G., Yu, H. and Yuan, B. (2001) Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets and Systems, 118, 9-19. Tseng, F., and Tzeng, G.（2002）A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets and Systems, 126,367-376. Wu, B. and Chen, M. (1999) Use of fuzzy statistical technique in change periods detection of nonlinear time series. Applied Mathematics and Computation, 99,241-254. Wu, B. and Hung, S. (1999) A fuzzy identification procedure for nonlinear time series: with example on ARCH and bilinear models. Fuzzy Sets and Systems, 108, 275-287. Xu, C. W. and Lee, Y. Z. (1987) Fuzzy Model Identification and Self-learning for Dynamic Systems. IEEE Transactions on Systems, Man, and Cybernetics, Vol. SCM-17, 683-689. Zadeh, L. A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. Zimmermann, H. J. (1991) Fuzzy Set Theory and Its Applications. Boston: Kluwer Academic. 描述 碩士
國立政治大學
應用數學系
89751012資料來源 http://thesis.lib.nccu.edu.tw/record/#A2010000263 資料類型 thesis dc.contributor.advisor 吳柏林 zh_TW dc.contributor.author (Authors) 楊勝斌 zh_TW dc.creator (作者) 楊勝斌 zh_TW dc.date (日期) 2002 en_US dc.date.accessioned 9-May-2016 16:39:22 (UTC+8) - dc.date.available 9-May-2016 16:39:22 (UTC+8) - dc.date.issued (上傳時間) 9-May-2016 16:39:22 (UTC+8) - dc.identifier (Other Identifiers) A2010000263 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/95621 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 應用數學系 zh_TW dc.description (描述) 89751012 zh_TW dc.description.abstract (摘要) 投資的獲利多寡並不單單基於預測的準確性，信心度的大小亦攸關獲利的結果。因為信心度愈大，則投資人將會提高投資的金額，而獲得更多的利潤。反之，雖然預測的結果是準確的，但若信心度很小，則投資人將不敢投入較多的金額，如此一來所獲得的利潤就有限了。本文嘗試著應用信心度函數來輔助說明多變量模糊時間數列預測結果，亦即預測模式對預測結果的屬性所具有的信心程度。最後利用多變量模糊時間數列模式，結合加權股價指數的收盤價及成交量兩個變量，針對台灣加權股價指數進行預測及衡量預測屬性的信心度。相信這對於風險控管及提高投資報酬深具意義。 zh_TW dc.description.abstract (摘要) 謝辭 摘要 目錄-----1 1. 前言-----2 2. 模糊時間數列分析與預測-----4 2.1 模糊邏輯之引進-----4 2.2 模糊時間數列分析-----5 2.3 如何由模糊規則庫進行屬性判別-----8 2.4 多變量模糊時間數列的預測-----11 2.5 平均預測秩階準確度-----12 3. 信心度函數-----13 3.1 信心函數-----13 3.2 如何建構與計算信心度-----15 4. 實證分析與結果-----19 4.1 資料分析-----19 4.2 模糊時間數列模式建構-----21 4.3 預測結果的比較與分析-----25 5. 結論-----28 參考文獻-----30 - dc.description.tableofcontents 謝辭 摘要 目錄-----1 1. 前言-----2 2. 模糊時間數列分析與預測-----4 2.1 模糊邏輯之引進-----4 2.2 模糊時間數列分析-----5 2.3 如何由模糊規則庫進行屬性判別-----8 2.4 多變量模糊時間數列的預測-----11 2.5 平均預測秩階準確度-----12 3. 信心度函數-----13 3.1 信心函數-----13 3.2 如何建構與計算信心度-----15 4. 實證分析與結果-----19 4.1 資料分析-----19 4.2 模糊時間數列模式建構-----21 4.3 預測結果的比較與分析-----25 5. 結論-----28 參考文獻-----30 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#A2010000263 en_US dc.subject (關鍵詞) 信心度函數 zh_TW dc.subject (關鍵詞) 模糊時間數列預測 zh_TW dc.subject (關鍵詞) 模糊規則庫 zh_TW dc.subject (關鍵詞) 平均預測秩階準確度 zh_TW dc.title (題名) 信心度函數與模糊時間數列預測 zh_TW dc.title (題名) Belief Function and Fuzzy Time Series Forecasting en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Chen, S. M. (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81, 311-319. Chiang, D., Chow, L., and Wang Y.(2000) Mining time series data by a fuzzy linguistic summary system. Fuzzy Sets and Systems, 112, 419-432. Graham, B. P. and Newell, R. B. (1989) Fuzzy adaptive control of a first-order process. Fuzzy Sets and Systems, 31, 47-65. Hendershot, G. and Placek, P. (Eds) (1981) Predicting Fertility. Lexington, MA: health and Co. K. Huarng（2001）Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems,123,369-386. Klir, G. F. and Folger, T. A. (1988) Fuzzy Sets, Uncertainly and Information. Englewood Cliffs, NJ:Prentice Hall. Lee, Y. C., Hwnag, C. and Shih, Y. P. (1994) A Combined Approach to Fuzzy Model Identification. IEEE Transactions on Systems, Man, and Cybernetics, Vol.24, No.5, 736-743. Manski, C. (1990) The Use of Intention Data to Predict Behavior: A Best Case Analysis. Journal of the American Statistical Association, 85, 934-940. Song, Q. and Chissom, B. S. (1993a) Forecasting enrollments with fuzzy time series─Part I. Fuzzy Sets and Systems, 54, 1-9. Song, Q. and Chissom, B. S. (1993b) Fuzzy time series and its models. Fuzzy Sets and Systems, 54, 269-277. Song, Q. and Chissom, B. S. (1994) Forecasting enrollments with fuzzy time series─Part II. Fuzzy Sets and Systems, 62, 1-8. Song, Q., Leland, R. P. and Chissom, B. S. (1997) Fuzzy stochastic fuzzy time series and its models. Fuzzy Sets and Systems, 88, 333-341. Tong, R. M. (1978) Synthesis of fuzzy models for industrial processes. Int. J. Gen. Syst., Vol.4, 143-162. Tseng, F., Tzeng, G., Yu, H. and Yuan, B. (2001) Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets and Systems, 118, 9-19. Tseng, F., and Tzeng, G.（2002）A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets and Systems, 126,367-376. Wu, B. and Chen, M. (1999) Use of fuzzy statistical technique in change periods detection of nonlinear time series. Applied Mathematics and Computation, 99,241-254. Wu, B. and Hung, S. (1999) A fuzzy identification procedure for nonlinear time series: with example on ARCH and bilinear models. Fuzzy Sets and Systems, 108, 275-287. Xu, C. W. and Lee, Y. Z. (1987) Fuzzy Model Identification and Self-learning for Dynamic Systems. IEEE Transactions on Systems, Man, and Cybernetics, Vol. SCM-17, 683-689. Zadeh, L. A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. Zimmermann, H. J. (1991) Fuzzy Set Theory and Its Applications. Boston: Kluwer Academic. zh_TW
