| dc.contributor.advisor | 吳柏林 | zh_TW |
| dc.contributor.advisor | Wu, Berlin | en_US |
| dc.contributor.author (Authors) | 李栢昌 | zh_TW |
| dc.contributor.author (Authors) | Li, Pai Chang | en_US |
| dc.creator (作者) | 李栢昌 | zh_TW |
| dc.creator (作者) | Li, Pai Chang | en_US |
| dc.date (日期) | 2012 | en_US |
| dc.date.accessioned | 2-Jan-2013 13:26:18 (UTC+8) | - |
| dc.date.available | 2-Jan-2013 13:26:18 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Jan-2013 13:26:18 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0099972006 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/56529 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 應用數學系數學教學碩士在職專班 | zh_TW |
| dc.description (描述) | 99972006 | zh_TW |
| dc.description (描述) | 101 | zh_TW |
| dc.description.abstract (摘要) | 台灣加權股價指數(TAIEX)，可以說是台灣最重要的經濟指數之一。在預測的方法中，時間序列分析一直都是熱門的課題，也是最常被使用來研究股價預測的方法。近年來，模糊理論在生醫、財務、社會、電機等各領域都有不錯的應用與發展 。本研究欲透過模糊區間的預測，主要是以時間序列預測台灣加權股價指數，來作為模糊區間精確度的探討，並針對區間時間序列進行模式的建構診斷和預測。最後我們將以2012年第一季(Q1)，每日交易股價指數的最高價與最低價作為實際研究的例子，同時也比較不同預測方法所得的結果。結果顯示模糊區間預測提供不同於傳統預測方法所得的資訊，希望能提供投資者另一種投資的參考。關鍵字 : 台灣加權股價指數(TAIEX) 、模糊理論、模糊區間、區間預測 | zh_TW |
| dc.description.abstract (摘要) | Taiwan Weighted Stock Index (TAIEX) is one of Taiwan`s most important economic indicators. Among the forecasting methods of time series analysis is always a hot issue on the forecasting methods and is also the most commonly used to make the stock price predictions. In recent years , fuzzy theory makes a great of application and development in various fields , such as , biomedical , financial and social …etc.. For this study, through the fuzzy interval forecasting is mainly based on time series forecasting TAIEX as fuzzy interval accuracy of the construction of diagnosis and prediction of the mode and interval time series. Finally, we will take the daily highest / lowest stock index prices data in the first quarter of 2012 (Q1) for actual research example , and will compare different forecasting methods of the results. The results show that the fuzzy interval forecasting differented from the traditional one on the basis of these information. We hope to offer investors an alternative investment advice.Keyword : Taiwan Capitalization Weighted Stock Index (TAIEX) 、 Fuzzy theory 、Fuzzy interval、Interval forecasting. | en_US |
| dc.description.tableofcontents | 1. 前言 12. 研究方法 32.1 時間序列預測方法 32.1.1 簡單移動平均法 ( Simple Moving Average Method ) 32.1.2 單一指數平滑法 ( Single Exponential Smoothing Method ) 32.1.3 自回歸移動平均法 (Autoregressive Integrated Moving Average Method ) 42.2 區間型模糊數及其運算 5定義 2.1 區間模糊數 5定義 2.2 區間模糊數加法 6定義 2.3 區間模糊數減法 6定義 2.4 反模糊值 72.3 模糊區間距離 7定義 2.5 模糊區間距離 (distance of interval) 7定義 2.6 區間平均誤差 ( Interval Mean Error ) 82.4 ARIMA 模型配適度檢測 8定義 2.7 赤池信息量準則 83. 實證分析 93.1 資料來源 93.2 預測方法探討 193.3 預測效率分析 204. 結論 225. 參考文獻 24 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0099972006 | en_US |
| dc.subject (關鍵詞) | 台灣加權股價指數 | zh_TW |
| dc.subject (關鍵詞) | 模糊理論 | zh_TW |
| dc.subject (關鍵詞) | 模糊區間 | zh_TW |
| dc.subject (關鍵詞) | 區間預測 | zh_TW |
| dc.subject (關鍵詞) | TAIEX. | en_US |
| dc.subject (關鍵詞) | Fuzzy theory | en_US |
| dc.subject (關鍵詞) | Fuzzy interval | en_US |
| dc.subject (關鍵詞) | Interval forecasting | en_US |
| dc.title (題名) | 模糊時間序列與區間預測方法探討-以台灣加權股價指數為例 | zh_TW |
| dc.title (題名) | A study on the Fuzzy time series and interval forecasting methods -with case study on the Taiwan Capitalization Weighted Stock Index | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | 中文部分[1] 吳柏林 1995 時間序列分析與導論 台北 華泰書局。[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 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 應用數學學報 第 25 卷 第一期 頁67-76[4] 林茂文1992 時間數列分析與預測 台北 華泰書局。[5] 王文俊 認識Fuzzy 第三版 2005。[6] 曾淑惠 2004 多變量模糊時間數列模式之應用以台灣地區高職教師人數 之預測為例 教育與心理研究 第 27 卷 第四期 頁845-861。英文部分[1] H. T. Nguyen and B. Wu (2006) Fundamentals of Statistics with Fuzzy Data. New York：Springer[2] S. M. Chen (1996) Forecasting enrollments based on fuzzy time series. Fuzzy sets and systems, 81, 311-319.[3] Wu, B. (1995). Model-free forecasting for nonlinear time series: with application in exchange rates. Computational Statistics and Data Analysis. 19, 433-459.[4] Wu, B., Chen, C. and Chen (1999) Application of time series analysis in quality control. Quality Control Journal. 35(7), 68-76.[5] Q. Song, B.S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets and Systems. Vol. 54, 1993, pp.269-277.[6] Q. Song, B.S. Chissom, “Forecasting enrollments with fuzzy time series-part I,” Fuzzy Sets and Systems. Vol. 54, 1993, pp.1-9.[7] Q. Song, B.S. Chissom, “Forecasting enrollments with fuzzy time series-part II,” Fuzzy Sets and Systems Vol. 62, 1994, pp.1-8. | zh_TW |
