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題名 模糊統計在時間數列分析與相似度之應用
Application of fuzzy statistics in time series analysis and similarity recognition
作者 張建瑋
貢獻者 吳柏林
張建瑋
關鍵詞 模糊統計
時間數列
相似度
Fuzzy statistics
Time series
Similarity
日期 1996
上傳時間 28-四月-2016 09:56:04 (UTC+8)
摘要 在時間數列的分析上,由於一些辨識模型結構的方法,常受制於時間數列本身的非定態及不確定干擾的影響,因此若以單一模式來配適數列往往不能得到滿意的結果。
An important problem in pattern recognition of a time series is similarity recognition. This paper presents the methods of similarity calculation for two time series. The methods considered include equally divided range method, K-rneans method and rank transformed method. The success of our similarity recognition relies a large extent on the fuzzy statistical concept. Simulation results demonstrate that, overall, the equally divided range method performed best in the similarity recognition. While other methods provide superior efficiency in calculating similarity for certain special time series. Finally two empirical examples, similarity calculating about GDP vs. Consumption and GDP vs. Invest, are illustrated.
描述 碩士
國立政治大學
應用數學系
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002002526
資料類型 thesis
dc.contributor.advisor 吳柏林zh_TW
dc.contributor.author (作者) 張建瑋zh_TW
dc.creator (作者) 張建瑋zh_TW
dc.date (日期) 1996en_US
dc.date.accessioned 28-四月-2016 09:56:04 (UTC+8)-
dc.date.available 28-四月-2016 09:56:04 (UTC+8)-
dc.date.issued (上傳時間) 28-四月-2016 09:56:04 (UTC+8)-
dc.identifier (其他 識別碼) B2002002526en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/87110-
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zh_TW
dc.description.abstract (摘要) 在時間數列的分析上,由於一些辨識模型結構的方法,常受制於時間數列本身的非定態及不確定干擾的影響,因此若以單一模式來配適數列往往不能得到滿意的結果。zh_TW
dc.description.abstract (摘要) An important problem in pattern recognition of a time series is similarity recognition. This paper presents the methods of similarity calculation for two time series. The methods considered include equally divided range method, K-rneans method and rank transformed method. The success of our similarity recognition relies a large extent on the fuzzy statistical concept. Simulation results demonstrate that, overall, the equally divided range method performed best in the similarity recognition. While other methods provide superior efficiency in calculating similarity for certain special time series. Finally two empirical examples, similarity calculating about GDP vs. Consumption and GDP vs. Invest, are illustrated.en_US
dc.description.tableofcontents 壹 前言-----1
     
     貳 理論與過程-----3
      2.1 模糊測度-----3
      2.2 相似性的探討-----4
      2.3 隸屬度函數的建構-----6
      2.4 時間數列模糊相似性演算法則-----9
     
     參 模擬與比較分析-----14
      3.1 模擬結果-----14
      3.2 比較分析-----19
     
     肆 實證分析-----24
     
     伍 結論與未來研究方向-----28
      5.1 結論-----28
      5.2 未來研究方向-----28
     
     參考文獻-----31
zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002002526en_US
dc.subject (關鍵詞) 模糊統計zh_TW
dc.subject (關鍵詞) 時間數列zh_TW
dc.subject (關鍵詞) 相似度zh_TW
dc.subject (關鍵詞) Fuzzy statisticsen_US
dc.subject (關鍵詞) Time seriesen_US
dc.subject (關鍵詞) Similarityen_US
dc.title (題名) 模糊統計在時間數列分析與相似度之應用zh_TW
dc.title (題名) Application of fuzzy statistics in time series analysis and similarity recognitionen_US
dc.type (資料類型) thesisen_US