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Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/60075
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dc.contributor.advisor吳柏林zh_TW
dc.contributor.author廖育琳zh_TW
dc.creator廖育琳zh_TW
dc.date2010en_US
dc.date.accessioned2013-09-04T07:14:09Z-
dc.date.available2013-09-04T07:14:09Z-
dc.date.issued2013-09-04T07:14:09Z-
dc.identifierG0096751004en_US
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/60075-
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.description96751004zh_TW
dc.description99zh_TW
dc.description.abstract雖然傳統線性時間數列在預測上已被廣泛的使用，但是在一般的時間數列中或多或少都會有結構改變(structural changes)的現象，我們往往很難找到一簡單的線性模式來詮釋資料中普遍存在的非線性(nonlinearity)結構，同時隨著模糊理論的興起與區間軟計算(soft computing)的發展，區間預測(interval forecasting)已成為未來研究的重點。本文應用模糊分類法(fuzzy classification)，找出結構改變的位置，藉此發展出非線性的區間門檻自迴歸模式(interval SETAR model)，再以「來臺觀光客人數」與「新臺幣兌美元匯率」作為實例，建構兩種區間門檻自迴歸模式與區間ARIMA模式並比較之，結果顯示兩種非線性的預測效果都比線性模式好。zh_TW
dc.description.tableofcontents第一章 前言........................................1\n第二章 研究方法.....................................4\n 2.1門檻自迴歸模式..............................4\n 2.2模糊隸屬度與模糊熵分類法.....................8\n 2.3區間ARIMA模式、區間門檻自迴歸模式............10\n 2.4預測效率評估...............................12\n第三章 實證分析-來臺觀光客人數.......................16\n 3.1資料來源...................................16\n3.2以區間型ARIMA模式建構............................18\n3.3平均累加模糊熵分類...............................19\n3.4以區間型門檻自迴歸模式建構........................22\n3.5預測結果比較與分析...............................28\n第四章 實證分析-新臺幣兌美元匯率......................30\n4.1資料來源........................................30\n4.2以區間型ARIMA模式建構............................32\n4.3平均累加模糊熵分類...............................33\n4.4以區間型門檻自迴歸模式建構........................36\n4.5預測結果比較與分析...............................42\n第五章 結論........................................44\n參考文獻...........................................46zh_TW
dc.format.extent1428441 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen_US-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0096751004en_US
dc.subject非線性zh_TW
dc.subject區間軟計算zh_TW
dc.subject門檻自迴歸zh_TW
dc.subject觀光客zh_TW
dc.subject匯率zh_TW
dc.title區間SETAR模式的建構分析與預測zh_TW
dc.titleInterval SETAR modelling and forecasting evaluationen_US
dc.typethesisen
dc.relation.reference中文部分：\n[1]. 交通部觀光局 (2002)。觀光客倍增計畫。\n[2]. 交通部觀光局 (2002)。觀光政策白皮書。\n[3]. 交通部觀光局 (2007)。旅遊台灣年計畫。\n[4]. 交通部觀光局 (2010)。中華民國年觀光年報。\n[5]. 吳柏林 (1995) 時間數列分析與導論。台北：華泰書局。\n[6]. 吳柏林、張建瑋 (1996)。非線性時間數列的分類與預測。第三屆三 軍官校基礎學術研討會論文集 98-214。\n[7]. 吳柏林 (2000)。模糊數學與統計應用。台北：俊傑書局。\n[8]. 吳柏林 (2005)。模糊統計導論:方法與應用。台北：五南出版社。\n[9]. 沈中華 (2000)。40分鐘學會匯率危機預測。台北：新陸書局。\n[10]. 李榮謙 (1999)。國際貨幣與金融。台北：智勝文化。\n[11]. 阮正治 (1996)。遺傳演算法在非線性時間數列結構改變之分析與應用。國立政治大學統計系碩士論文。\n[12]. 林茂文 (1992)。時間序列分析與預測。台北：華泰書局。\n[13]. 林原宏 (2006)。模糊統計。台北：五南出版社。\n[14]. 程友梅 (1995)。轉折型時間序列的認定。國立政治大學統計系碩士論文。\n[15]. 張新發 (1996)。遺傳演算法在門檻自迴歸模式(d,r)值估計的應用。國立政治大學統計系碩士論文。\n[16]. 楊奕農 (2006)。時間序列分析-經濟與財務上之應用。台北：雙葉書廊。\n[17]. 賈昭南 (2002)。國際金融實務與理論。台北：華泰文化。\n\n英文部分：\n[1]. Chang, S.K. (2007). On the Testing Hypotheses of Mean and Variance for Interval Data. Management Science and Statistical Decision 4(2), 63-69.\n[2]. Chatfield, C. (1993). Calculating Interval Forecasts. Journal and Business & Economic Statistics 11(2), 121-135.\n[3]. Chen, S.M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy sets and systems 81, 311-319.\n[4]. Huarng, K. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy sets and systems 123(3), 387-394.\n[5]. Hsu, H.L. (2008). Evaluating forecasting performance for interval data. Computers and Mathematics with Applications 56, 2155-2163.\n[6]. Kashia, M., Hejaz, S.R. and Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy sets and systems 159, 769-786.\n[7]. Kreinovich, V., Nguyen, H.T. and Wu, B. (2007). On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems. Information \nSciences 177, 3228-3238.\n[8]. Ludermir, T.B. (2008). Forecasting models for interval-valued time series. Neurocomputing 71, 3344-3352.\n[9]. Nguyen, H.T. and Wu, B. (2006). Fundamentals of Statistics with Fuzzy Data. New York：Springer.\n[10]. Römer, C. and Kandel, A. (2000). Statistical tests for fuzzy data. Fuzzy sets and systems 72(1), 1-26.\n[11]. Tong, R.M. (1978). Synthesis of fuzzy models for industrial processes. International Journal of General Systems 5(4), 143-162.\n[12]. Tsay, R.S. (1991). Detecting and modeling non-linearity in univariate time series Analysis. Statistica Sinica 1(2), 431-451.\n[13]. Tseng, F.M., Tseng, G.H., Yu, H.C., and Yuan, B.C. (2001). Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy sets and systems 118(1),9-19.\n[14]. Tseng, F.M. and Tseng, G.H. (2002). A fuzzy seasonal ARIMA model for forecasting. Fuzzy sets and systems 126(3), 367-376.zh_TW
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