| dc.contributor.advisor | 吳柏林 | zh_TW |
| dc.contributor.author (Authors) | 廖俊銘 | zh_TW |
| dc.creator (作者) | 廖俊銘 | zh_TW |
| dc.date (日期) | 2009 | en_US |
| dc.date.accessioned | 11-Oct-2011 16:56:05 (UTC+8) | - |
| dc.date.available | 11-Oct-2011 16:56:05 (UTC+8) | - |
| dc.date.issued (上傳時間) | 11-Oct-2011 16:56:05 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0094972005 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/51586 | - |
| 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) | 94972005 | zh_TW |
| dc.description (描述) | 98 | zh_TW |
| dc.description.abstract (摘要) | 近年來，隨著科技的進步與工商業的發展，預測技術的創新與改進愈來愈受到重視，同樣地，對於預測準確度的要求也愈來愈高。尤其在經濟建設、人口政策、經營規畫、管理控制等問題上，預測更是決策過程中不可或缺的重要資訊。有鑑於此，本論文嘗試應用模糊關係方程式，提出多變量模糊時間數列建構過程及轉折區間檢測模式理論架構。另一方面，多變量模糊時間數列模式建構過程，研究者曾提出很多轉折點之偵測與檢定方法，然而在實際的例子中，時間數列之結構改變所呈現出來的是一種緩慢的改變過程，即轉折點本身就是模糊不確定。這個概念在建構不同模式分析各國經濟活動數據時更顯重要。本論文針對轉折區間之檢測提出一個完整的認定程序。多變量時間數列系統中的隸屬度函數等於在計算成果指標群時的群集中心。應用本論文提出的方法，我們以德國、法國及希臘之總體經濟指標GDP為例，考慮通貨膨脹率、GDP成長率及投資率來進行轉折區間的檢測。 | zh_TW |
| dc.description.abstract (摘要) | In recent years, along with the technological advancement and commercial development, the creation and improvement of forecasting techniques have more and more attention. Especially at the economic developments, population policy, management planning and control, forecasting gives necessary and important information in the decision-making process. Regarding stock market as the example, these numerals of closing price are uncertain and indistinct. Again, the factors of influence on quantity are numerous, such as turnover, exchange rate etc. Therefore, if we consider merely the closing price of front day to build and forecast, we will not only misestimate the future trend, but also will cause unnecessary damage.Owing to this reason, we propose the procedure of multivariate fuzzy time series model constructed and theory structure by fuzzy relation equation. Combining closing price with turnover, we apply our methods to build up multivariate fuzzy time series model on Taiwan Weighted Index and predict future trend while examine the predictive results with average forecasting accuracy.A fuzzy time series is defined on averages of cumulative fuzzy entropies of the tree time series. Finally, an empirical study about change periods identification for Germany, France and Greece major macroeconomic indicators are demonstrated. | en_US |
| dc.description.tableofcontents | 多變量模糊時間數列分析與轉折區間檢測 1第一章 前言 4第二章 多變量模糊時間數列模式建構與預測 72.1 多變量模糊時間數列模式建構 72.2 如何由模糊規則庫進行屬性判別 82.3 多變量模糊時間數列之預測 112.4 平均預測秩階準確度 12第三章 模糊決策過程 143.1 模糊轉折 143.2 群落中心 15第四章 實證分析 19第五章 結論 23參考文獻 24 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0094972005 | en_US |
| dc.subject (關鍵詞) | 模糊關係 | zh_TW |
| dc.subject (關鍵詞) | 模糊馬可夫關係矩陣 | zh_TW |
| dc.subject (關鍵詞) | 多變量模糊時間數列 | 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.subject (關鍵詞) | Fuzzy relation | en_US |
| dc.subject (關鍵詞) | fuzzy markov relative matrix | en_US |
| dc.subject (關鍵詞) | multivariate fuzzy time series | en_US |
| dc.subject (關鍵詞) | fuzzy rule base | en_US |
| dc.subject (關鍵詞) | average forecasting accuracy | en_US |
| dc.title (題名) | 多變量模糊時間數列分析與轉折區間檢測 | zh_TW |
| dc.title (題名) | Multivariate Fuzzy Time Series Analysis with Change Periods Detection | en_US |
| dc.type (資料類型) | thesis | en |
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