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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/37087


    Title: 多變量模糊時間數列在財務上的應用
    An Application of Multivariate Fuzzy Time Series on Financial Markets.
    Authors: 呂冠宏
    Contributors: 吳柏林
    呂冠宏
    Keywords: 模糊時間數列
    模糊關係矩陣
    預測
    Date: 2007
    Issue Date: 2009-09-19 12:07:20 (UTC+8)
    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。
    Reference: 中文部份
    [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
    為例," 中國統計學報, Vol.25, No.1, pp.67-76.
    [2] 吳柏林 (2005), 模糊統計導論, 方法與應用. 台北:五南書局
    [3] 吳柏林 (1995), 時間序列分析導論. 台北:華泰書局
    [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)
    英文部分
    [5] Dug Hun Hong (2005), A note on fuzzy time-series model. Fuzzy Sets and Systems, 155, 309-316.
    [6] Diebold F. X. and Lindner P. (1996), Fractional integration and interval prediction. Economics Letters, 50, 305-313.
    [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.
    [8] Huarng, K. (2001), Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems, 123, 387-394.
    [9] Huarng, K. (2001), Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems, 123, 369-386.
    [10] Huarng, K. and Yu T. H. (2006), The application of neural networks to forecast fuzzy time series. Physica A, 363, 481-491.
    [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.
    [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.
    [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.
    [14] Song, Q. and Chissom, B.S. (1993a), Forecasting enrollments with fuzzy time
    series – part I. Fuzzy Sets and Systems, 54, 1-9.
    [15] Song, Q. and Chissom, B.S. (1993b), Fuzzy time series and its models. Fuzzy Sets and Systems, 54, 269-277.
    [16] Song, Q. and Chissom, B.S. (1994), Forecasting enrollments with fuzzy time
    series – part II. Fuzzy Sets and Systems, 62, 1-8.
    [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.
    [18] Song, Q., Leland, R. P. and Chissom, B. S. (1997), Fuzzy stochastic fuzzy time
    series and its models. Fuzzy Sets and Systems, 62, 1-8.
    [19] Chen, S. (1996), Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81, 311-319.
    [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.
    [21] Zimmermann, H.J. (1991), Fuzzy Set Theory and Its Applications. Boston:Kluwer Academi.
    Description: 碩士
    國立政治大學
    應用數學研究所
    93751009
    96
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0093751009
    Data Type: thesis
    Appears in Collections:[Department of Mathematical Sciences] Theses

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