Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/54646| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 吳柏林 | zh_TW |
| dc.contributor.advisor | Wu, Berlin | en_US |
| dc.contributor.author | 吳佩容 | zh_TW |
| dc.contributor.author | Wu, Pei Jung | en_US |
| dc.creator | 吳佩容 | zh_TW |
| dc.creator | Wu, Pei Jung | en_US |
| dc.date | 2011 | en_US |
| dc.date.accessioned | 2012-10-30T03:27:58Z | - |
| dc.date.available | 2012-10-30T03:27:58Z | - |
| dc.date.issued | 2012-10-30T03:27:58Z | - |
| dc.identifier | G0099751003 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/54646 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 應用數學研究所 | zh_TW |
| dc.description | 99751003 | zh_TW |
| dc.description | 100 | zh_TW |
| dc.description.abstract | 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.................................. 3\n2. 區間模糊數與預測效率分析.............. 5\n2.1 模糊時間數列..................... 5\n2.2 常見的區間時間數列預測模式....... 6\n2.3 預測效率評估..................... 9\n3. 研究方法.............................. 12\n3.1 加權時間數列法................... 12\n3.2 加權模糊時間數列法............... 16\n4. 實證分析.............................. 17\n4.1 資料來源......................... 17\n4.2 加權模糊時間數列法............... 17\n4.3 左右端點k階區間移動平均法........ 22\n4.4 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：................. 27\n5. 結論.................................. 28\n參考目錄................................. 29 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0099751003 | en_US |
| dc.subject | 模糊時間數列分析 | zh_TW |
| dc.subject | 預測 | zh_TW |
| dc.subject | 整合測度 | zh_TW |
| dc.subject | 效率評估 | zh_TW |
| dc.title | 加權模糊時間數列分析與預測效率評估 | zh_TW |
| dc.title | Analysis and Efficiency Evaluation with Forecasting for Weighted Fuzzy Time Series | en_US |
| dc.type | thesis | en |
| dc.relation.reference | [1] 吳柏林 2005模糊統計導論與應用。五南書局。\n[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。\n[3] 吳柏林 1995 時間數列分析導論。華泰書局。\n[4] 林茂文 1992 時間序列分析與預測。華泰書局。\n[5] 林原宏 2006 模糊統計。五南書局。\n[6] 楊奕農 2009 時間序列分析-經濟與財務上之應用。雙葉書廊。\n[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.\n[8] Chatfield, C. (1993). “Calculating Interval Forecasts,”Journal and Business & Economic Statics, Vol. 11, No. 2, pp. 121-135.\n[9] Chen, S. M. (1996). “Forecasting Enrollments Based on Fuzzy Time Series,”Fuzzy Sets and Systems, Vol. 81, No. 3, pp. 311-319.\n[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.\n[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.\n[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.\n[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.\n[14] Hsu, H. L. (2008). “Interval Time Series Analysis with Forecasting Efficiency Evaluation,” Doctorial Thesis, Department of Mathematical Science, National Chengchi University, Taipei, Taiwan.\n[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.\n[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.\n[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.\n[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.\n[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.\n[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.\n[21] Zadeh, L. A. (1965). “Fuzzy Sets,” Information Control, Vol. 8, No. 3, pp. 338-353. | zh_TW |
| item.fulltext | With Fulltext | - |
| item.languageiso639-1 | en_US | - |
| item.grantfulltext | open | - |
| item.cerifentitytype | Publications | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.openairetype | thesis | - |
| Appears in Collections: | 學位論文 | |
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| 100301.pdf | 1.71 MB | Adobe PDF2 | View/Open | |
| 100302.pdf | 95.98 kB | Adobe PDF2 | View/Open |
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