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Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/37087
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dc.contributor.advisor吳柏林zh_TW
dc.contributor.author呂冠宏zh_TW
dc.creator呂冠宏zh_TW
dc.date2007en_US
dc.date.accessioned2009-09-19T04:07:20Z-
dc.date.available2009-09-19T04:07:20Z-
dc.date.issued2009-09-19T04:07:20Z-
dc.identifierG0093751009en_US
dc.identifier.urihttps://nccur.lib.nccu.edu.tw/handle/140.119/37087-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description應用數學研究所zh_TW
dc.description93751009zh_TW
dc.description96zh_TW
dc.description.abstract股票是許多人採取投資的項目。若能準確預測股價的漲跌，則可以有效地降低投資風險，賺取利潤。然而，有許多因素會影響股票走勢，例如政治因素，匯率變化，天災人禍。因此，股票走勢很難被精確預測。我們嘗試用模糊統計來解決股價預測的問題。本論文藉由模糊相關矩陣來建立多變量模糊時間數列，以便用來預測股票趨勢。實證研究則以台灣加權股價指數為對象，對每日的收盤價進行模糊時間數列分析與預測，還計算誤差與準確率。實證研究顯示，能降低投資者的風險。zh_TW
dc.description.tableofcontents1. 前言 1\n2. 模糊時間數列模式建構 4\n2.1 模糊邏輯 4\n2.2 模式建立 5\n2.3 FAR(1)模式建構 9\n2.4 FAR(P)模式建構 13\n2.5 VFAR(1,2)模式建構 14\n3. 實証分析 - 點估計 16\n3.1 資料分析 16\n3.2 計算模糊矩陣 17\n3.3 FAR(1)模式預測結果 19\n3.4 FAR(2)模式預測結果 25\n3.5 VFAR(1,2)模式預測結果 28\n4. 實證分析 - 區間估計 33\n5. 結論 37\n附錄1 39\n附錄2 40\n參考文獻 42zh_TW
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dc.language.isoen_US-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0093751009en_US
dc.subject模糊時間數列zh_TW
dc.subject模糊關係矩陣zh_TW
dc.subject預測zh_TW
dc.title多變量模糊時間數列在財務上的應用zh_TW
dc.titleAn Application of Multivariate Fuzzy Time Series on Financial Markets.en_US
dc.typethesisen
dc.relation.reference中文部份zh_TW
dc.relation.reference[1] 吳柏林;林玉鈞, (2002), \"模糊時間數列分析與預測：以台灣地區加權股價指數zh_TW
dc.relation.reference為例,\" 中國統計學報, Vol.25, No.1, pp.67-76.zh_TW
dc.relation.reference[2] 吳柏林 (2005), 模糊統計導論, 方法與應用. 台北:五南書局zh_TW
dc.relation.reference[3] 吳柏林 (1995), 時間序列分析導論. 台北:華泰書局zh_TW
dc.relation.reference[4] 陳蒼山 (2006),模糊時間數列分析與預測—以石油價格為例(碩士論文)zh_TW
dc.relation.reference英文部分zh_TW
dc.relation.reference[5] Dug Hun Hong (2005), A note on fuzzy time-series model. Fuzzy Sets and Systems, 155, 309-316.zh_TW
dc.relation.reference[6] Diebold F. X. and Lindner P. (1996), Fractional integration and interval prediction. Economics Letters, 50, 305-313.zh_TW
dc.relation.reference[7] Li, H., Miao, Z., Han, S. and Wang, J. (2005), A new kind of fuzzy relation equations based on inner transformation. Computers and Mathematics with Applications, 50, 623-636.zh_TW
dc.relation.reference[8] Huarng, K. (2001), Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems, 123, 387-394.zh_TW
dc.relation.reference[9] Huarng, K. (2001), Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems, 123, 369-386.zh_TW
dc.relation.reference[10] Huarng, K. and Yu T. H. (2006), The application of neural networks to forecast fuzzy time series. Physica A, 363, 481-491.zh_TW
dc.relation.reference[11] Koutroumanidis T., Iliadis L. and Sylaios G. K. (2006), Time-series modeling of fishery landings using ARIMA models and fuzzy expected intervals software. Environmental Modeling ＆ Software, 21, 1711-1721.zh_TW
dc.relation.reference[12] Ramsés H. Mena and Sephen G. Walker (2005), Stationary autoregressive models via a Bayesian nonparametric approach. Journal of Time Series Analysis, 26, 789-805.zh_TW
dc.relation.reference[13] Rob J. H, Anne B.K, J. Keith Ord and Ralph D. S (2005), Prediction intervals for exponential smoothing using two new classes of state space models. Journal of Forecasting, 24, 17-37.zh_TW
dc.relation.reference[14] Song, Q. and Chissom, B.S. (1993a), Forecasting enrollments with fuzzy timezh_TW
dc.relation.referenceseries – part I. Fuzzy Sets and Systems, 54, 1-9.zh_TW
dc.relation.reference[15] Song, Q. and Chissom, B.S. (1993b), Fuzzy time series and its models. Fuzzy Sets and Systems, 54, 269-277.zh_TW
dc.relation.reference[16] Song, Q. and Chissom, B.S. (1994), Forecasting enrollments with fuzzy timezh_TW
dc.relation.referenceseries – part II. Fuzzy Sets and Systems, 62, 1-8.zh_TW
dc.relation.reference[17] Song, Q., Leland, R. P. and Chissom, B. S. (1995), A new fuzzy time-series model of fuzzy number observations. Fuzzy Sets and Systems, 73, 341-348.zh_TW
dc.relation.reference[18] Song, Q., Leland, R. P. and Chissom, B. S. (1997), Fuzzy stochastic fuzzy timezh_TW
dc.relation.referenceseries and its models. Fuzzy Sets and Systems, 62, 1-8.zh_TW
dc.relation.reference[19] Chen, S. (1996), Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81, 311-319.zh_TW
dc.relation.reference[20] 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.zh_TW
dc.relation.reference[21] Zimmermann, H.J. (1991), Fuzzy Set Theory and Its Applications. Boston:Kluwer Academi.zh_TW
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