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題名 加權模糊時間數列分析與預測效率評估
Analysis and Efficiency Evaluation with Forecasting for Weighted Fuzzy Time Series作者 吳佩容
Wu, Pei Jung貢獻者 吳柏林
Wu, Berlin
吳佩容
Wu, Pei Jung關鍵詞 模糊時間數列分析
預測
整合測度
效率評估日期 2011 上傳時間 30-十月-2012 11:27:58 (UTC+8) 摘要 近年來，預測技術的創新與改進愈來愈受到重視。對於預測效率評估的要求也愈來愈高。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。目前有關模糊時間數列分析與預測效率評估並不多見。主要是模糊殘差值的測量相當困難。有鑑於此，本文提出以模糊距離來進行效率評估。並且從不同的角度來探討預測的準確度。實證研究顯示，藉由中心點與區間長度的整合測度，可以得到一個合理的評估結果。這對於財務金融的模糊數據分析與未來市場的走勢將深具意義。 參考文獻 [1] 吳柏林 2005模糊統計導論與應用。五南書局。[2] 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。[3] 吳柏林 1995 時間數列分析導論。華泰書局。[4] 林茂文 1992 時間序列分析與預測。華泰書局。[5] 林原宏 2006 模糊統計。五南書局。[6] 楊奕農 2009 時間序列分析-經濟與財務上之應用。雙葉書廊。[7] Chang, S. K. (2007). “On the Testing Hypotheses of Mean and Variance for Interval Data,”Management Science & Statistical Decision, Vol. 4, No. 2, pp. 63-69.[8] Chatfield, C. (1993). “Calculating Interval Forecasts,”Journal and Business & Economic Statics, Vol. 11, No. 2, pp. 121-135.[9] Chen, S. M. (1996). “Forecasting Enrollments Based on Fuzzy Time Series,”Fuzzy Sets and Systems, Vol. 81, No. 3, pp. 311-319.[10] Chen, S. M. (2002). “Forecasting Enrollments Based on High Order Fuzzy Time Series,”Cybernetics and Systems: An International Journal, Vol. 133, No. 1, pp. 1-16.[11] Chen, S. M. and Hsu, C. C. (2004). “A New Method to Forecast Enrollment Using Fuzzy Time Series,”International Journal of Applied Science and Engineering, Vol. 3, No. 2, pp. 234-244.[12] Cheng, C. H., Chen, T. L., and Chiang C. H. (2006). “Trend-Weighted Fuzzy Time Series Model for TAIEX Forecasting,”Proceeding of the 13th International Conference on Neural Information Processing, Part-Ⅲ, Lecture Notes in Computer Science, Hong Kong, Vol. 4234, pp. 469-477.[13] Huarng, K. (2001). “Effective Lengths of Intervals to Improve Forecasting in Fuzzy Time Series,”Fuzzy Sets and Systems, Vol. 123, No. 3, pp.387-394.[14] Hsu, H. L. (2008). “Interval Time Series Analysis with Forecasting Efficiency Evaluation,” Doctorial Thesis, Department of Mathematical Science, National Chengchi University, Taipei, Taiwan.[15] Hsu, Y.Y., Tse, S.M. and Wu, B. (2003). “A New Approach of Bivariate Fuzzy Time Series Analysis to the Forecasting of a Stock Index,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 11, No. 6, pp. 671-690.[16] Kreinovich, V., Nguyen H. T., and Wu, B. (2006). “On-Line Algorithms for Computing Mean and Variance of Interval Data, and their Use in Intelligent Systems,”Information Science, Vol. 177, pp. 3228-3238.[17] Pathak, H. K. and Singh, P. (2011). ”A New Bandwidth Interval Based Forecasting Method for Enrollments Using Fuzzy Time Series,”Scientific Research, Vol. 2, pp. 504-507.[18] Song, Q. and Chissom, B. S. (1993). “Forecasting Enrollment with Fuzzy Time Series-Part Ⅰ,”Fuzzy Sets and Systems, Vol. 54, No. 1, pp. 1-9.[19] Tseng, F.-M. and Tzeng, G.-H. (2002). “A Fuzzy Seasonal ARIMA Model for Forecasting,” Fuzzy Sets and Systems, Vol. 126, No. 3, pp. 367-376.[20] Wu, B. and Hung, S. (2006). “A Fuzzy Identification Procedure for Nonlinear Time Series with Example on ARCH and Bilinear Models,”Fuzzy Sets and Systems, Vol. 108, pp. 275-287.[21] Zadeh, L. A. (1965). “Fuzzy Sets,” Information Control, Vol. 8, No. 3, pp. 338-353. 描述 碩士
國立政治大學
應用數學研究所
99751003
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099751003 資料類型 thesis dc.contributor.advisor 吳柏林 zh_TW dc.contributor.advisor Wu, Berlin en_US dc.contributor.author (作者) 吳佩容 zh_TW dc.contributor.author (作者) Wu, Pei Jung en_US dc.creator (作者) 吳佩容 zh_TW dc.creator (作者) Wu, Pei Jung en_US dc.date (日期) 2011 en_US dc.date.accessioned 30-十月-2012 11:27:58 (UTC+8) - dc.date.available 30-十月-2012 11:27:58 (UTC+8) - dc.date.issued (上傳時間) 30-十月-2012 11:27:58 (UTC+8) - dc.identifier (其他 識別碼) G0099751003 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54646 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 應用數學研究所 zh_TW dc.description (描述) 99751003 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 近年來，預測技術的創新與改進愈來愈受到重視。對於預測效率評估的要求也愈來愈高。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。目前有關模糊時間數列分析與預測效率評估並不多見。主要是模糊殘差值的測量相當困難。有鑑於此，本文提出以模糊距離來進行效率評估。並且從不同的角度來探討預測的準確度。實證研究顯示，藉由中心點與區間長度的整合測度，可以得到一個合理的評估結果。這對於財務金融的模糊數據分析與未來市場的走勢將深具意義。 zh_TW dc.description.tableofcontents 1. 前言.................................. 32. 區間模糊數與預測效率分析.............. 52.1 模糊時間數列..................... 52.2 常見的區間時間數列預測模式....... 62.3 預測效率評估..................... 93. 研究方法.............................. 123.1 加權時間數列法................... 123.2 加權模糊時間數列法............... 164. 實證分析.............................. 174.1 資料來源......................... 174.2 加權模糊時間數列法............... 174.3 左右端點k階區間移動平均法........ 224.4 比較「加權模糊時間數列法」及「左右端點k階區間移動平均法」 的測量誤差：................. 275. 結論.................................. 28參考目錄................................. 29 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099751003 en_US dc.subject (關鍵詞) 模糊時間數列分析 zh_TW dc.subject (關鍵詞) 預測 zh_TW dc.subject (關鍵詞) 整合測度 zh_TW dc.subject (關鍵詞) 效率評估 zh_TW dc.title (題名) 加權模糊時間數列分析與預測效率評估 zh_TW dc.title (題名) Analysis and Efficiency Evaluation with Forecasting for Weighted Fuzzy Time Series en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] 吳柏林 2005模糊統計導論與應用。五南書局。[2] 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。[3] 吳柏林 1995 時間數列分析導論。華泰書局。[4] 林茂文 1992 時間序列分析與預測。華泰書局。[5] 林原宏 2006 模糊統計。五南書局。[6] 楊奕農 2009 時間序列分析-經濟與財務上之應用。雙葉書廊。[7] Chang, S. K. (2007). “On the Testing Hypotheses of Mean and Variance for Interval Data,”Management Science & Statistical Decision, Vol. 4, No. 2, pp. 63-69.[8] Chatfield, C. (1993). “Calculating Interval Forecasts,”Journal and Business & Economic Statics, Vol. 11, No. 2, pp. 121-135.[9] Chen, S. M. (1996). “Forecasting Enrollments Based on Fuzzy Time Series,”Fuzzy Sets and Systems, Vol. 81, No. 3, pp. 311-319.[10] Chen, S. M. (2002). “Forecasting Enrollments Based on High Order Fuzzy Time Series,”Cybernetics and Systems: An International Journal, Vol. 133, No. 1, pp. 1-16.[11] Chen, S. M. and Hsu, C. C. (2004). “A New Method to Forecast Enrollment Using Fuzzy Time Series,”International Journal of Applied Science and Engineering, Vol. 3, No. 2, pp. 234-244.[12] Cheng, C. H., Chen, T. L., and Chiang C. H. (2006). “Trend-Weighted Fuzzy Time Series Model for TAIEX Forecasting,”Proceeding of the 13th International Conference on Neural Information Processing, Part-Ⅲ, Lecture Notes in Computer Science, Hong Kong, Vol. 4234, pp. 469-477.[13] Huarng, K. (2001). “Effective Lengths of Intervals to Improve Forecasting in Fuzzy Time Series,”Fuzzy Sets and Systems, Vol. 123, No. 3, pp.387-394.[14] Hsu, H. L. (2008). “Interval Time Series Analysis with Forecasting Efficiency Evaluation,” Doctorial Thesis, Department of Mathematical Science, National Chengchi University, Taipei, Taiwan.[15] Hsu, Y.Y., Tse, S.M. and Wu, B. (2003). “A New Approach of Bivariate Fuzzy Time Series Analysis to the Forecasting of a Stock Index,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 11, No. 6, pp. 671-690.[16] Kreinovich, V., Nguyen H. T., and Wu, B. (2006). “On-Line Algorithms for Computing Mean and Variance of Interval Data, and their Use in Intelligent Systems,”Information Science, Vol. 177, pp. 3228-3238.[17] Pathak, H. K. and Singh, P. (2011). ”A New Bandwidth Interval Based Forecasting Method for Enrollments Using Fuzzy Time Series,”Scientific Research, Vol. 2, pp. 504-507.[18] Song, Q. and Chissom, B. S. (1993). “Forecasting Enrollment with Fuzzy Time Series-Part Ⅰ,”Fuzzy Sets and Systems, Vol. 54, No. 1, pp. 1-9.[19] Tseng, F.-M. and Tzeng, G.-H. (2002). “A Fuzzy Seasonal ARIMA Model for Forecasting,” Fuzzy Sets and Systems, Vol. 126, No. 3, pp. 367-376.[20] Wu, B. and Hung, S. (2006). “A Fuzzy Identification Procedure for Nonlinear Time Series with Example on ARCH and Bilinear Models,”Fuzzy Sets and Systems, Vol. 108, pp. 275-287.[21] Zadeh, L. A. (1965). “Fuzzy Sets,” Information Control, Vol. 8, No. 3, pp. 338-353. zh_TW
