| dc.contributor.advisor | 吳柏林 | zh_TW |
| dc.contributor.author (Authors) | 廖敏治 | zh_TW |
| dc.creator (作者) | 廖敏治 | zh_TW |
| dc.date (日期) | 1998 | en_US |
| dc.date.accessioned | 14-Apr-2016 13:57:23 (UTC+8) | - |
| dc.date.available | 14-Apr-2016 13:57:23 (UTC+8) | - |
| dc.date.issued (上傳時間) | 14-Apr-2016 13:57:23 (UTC+8) | - |
| dc.identifier (Other Identifiers) | A2002000638 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/84406 | - |
| dc.description (描述) | 博士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計學系 | zh_TW |
| dc.description.abstract (摘要) | 本文將模糊理論的觀念，應用到時間數列分析上。研究重點包括模糊自相似度的定義與度量，模糊自迴歸係數的分析，模糊相似度辨識與自迴歸階次認定、模糊時間數列模式建構與預測等。我們首先給定模糊時間數列模式的概念與一些重要性質。接著提出模糊相似度的定義與度量，以及模式建構的流程。經由系統性的模擬與分析，我們建立階次認定的演算法則與認定程序。藉著詳細的演算比較這些類型的模糊時間數列。並以模糊關係方程式推導，提出合適的模糊時間數列模式建構方法。並利用提出的方法對台灣的景氣對策信號，及台灣結婚率建立模糊時間數列模式。最後，使用所建構的模糊時間數列模式對未來進行預測，以驗證所建構模糊時間數列模式的效率性與實用性。 | zh_TW |
| dc.description.abstract (摘要) | In modeling a time series the accuracy of various model constructions and forecasting techniques, certain rules and models are adhered to. Traditional methods on the model construction for a time series are based on the researchers` experience by choosing a "good" model, which will satisfactorily explain its dynamic behavior, from a model-base. But a fundamental question that often arises is: does the data exhibit the real case honestly? In this research we show how fuzzy time series construction be applied for this purpose. An order detection process for fuzzy time series is presented. Simulation has been used extensively to explore general properties of statistical procedures, and the approach is particularly useful in fuzzy time series construction. Statistical strategies typically consist of sequences of rules used repeatedly on the same data set. | en_US |
| dc.description.tableofcontents | 封面頁證明書致謝詞論文摘要目錄表目錄圖目錄1.前言2.模糊集合理論與模糊時間數列2.1模糊測度2.2隸屬度函數與模糊時間數列分析2.3模糊時間數列類型的探討3.模糊時間數列模式的建構3.1模糊趨勢(FUZZY TREND)與模糊季節性(FUZZY SEASONAL)3.2模糊關係矩陣R的計算3.3糊時間數列階次的認定3.4模糊時間數列模式的建構步驟及流程4.模糊時間數列階次認定模擬4.1模擬一階模糊時間數列及其自相似度4.2模擬二階模糊時間數列及其自相似度4.3擬結果探討4.4階次判定演算法則5.實例5.1景氣對策信號5.2結婚率5.3註冊人數6.結論參考文獻附錄 | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#A2002000638 | en_US |
| dc.subject (關鍵詞) | 模糊時間數列 | zh_TW |
| dc.subject (關鍵詞) | 階次認定 | zh_TW |
| dc.subject (關鍵詞) | 模式建構 | zh_TW |
| dc.subject (關鍵詞) | 模糊自相似度 | zh_TW |
| dc.subject (關鍵詞) | 隸屬度函數 | zh_TW |
| dc.subject (關鍵詞) | 模糊趨勢 | zh_TW |
| dc.subject (關鍵詞) | 模糊季節性 | zh_TW |
| dc.subject (關鍵詞) | Fuzzy time series | en_US |
| dc.subject (關鍵詞) | Order identification | en_US |
| dc.subject (關鍵詞) | Models construction | en_US |
| dc.subject (關鍵詞) | Fuzzy auto-similarity | en_US |
| dc.subject (關鍵詞) | Membership function | en_US |
| dc.subject (關鍵詞) | Fuzzy trend | en_US |
| dc.subject (關鍵詞) | Fuzzy seasonal | en_US |
| dc.title (題名) | 模糊時間數列的階次認定、模式建構及預測 | zh_TW |
| dc.title (題名) | The Order Identification of Fuzzy Time Series, Models Construction and Forecasting | en_US |
| dc.type (資料類型) | thesis | en_US |
