政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/60077

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/60077

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政大典藏 > College of Science > Department of Mathematical Sciences > Theses > Item 140.119/60077

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/60077

Title:	加權模糊時間數列在區間預測上之應用 The application of weighted fuzzy time series to Interval forecasting
Authors:	潘俊延 Pan, Chun Yen
Contributors:	吳柏林 Wu, Berlin 潘俊延 Pan, Chun Yen
Keywords:	模糊時間數列
Date:	2010
Issue Date:	2013-09-04 15:14:34 (UTC+8)
Abstract:	預測技術在決策過程中是不可或缺的重要工具。精確的預測可以提供決策者更多的資訊去做出正確的決策。傳統的點預測方法是目前使用最多的預測方式，其預測模式常需要較嚴格的基本假設，這使得預測模式的建構較為困難。而加權模糊時間數列模式並不需要強烈的基本假設，模式架構較傳統更為簡易，也提供決策者更多的選擇。本研究將傳統的加權模糊時間數列推廣為區間加權模糊時間數列。與常用的幾種區間模糊時間數列做比較，以預測每日台幣對美元的匯率的方式來探討幾種預測方法的效率評估與準確性。 Forecasting technology has played an important role for the decision makers. Accurate forecasts can provide decision makers more information to make the right decisions. Currently, the most use of forecasts is the traditional point forecasting, whose forecasting model often requires strict assumptions, and this makes it more difficult to construct the forecasting model. Weighted fuzzy time series model does not require so strong assumptions, so the model construction is simpler than traditional ones. It also provides the decision makers more options. In this research, we promote the weighted fuzzy time series model to the interval weighted fuzzy time series model. And we compare it with some commonly used interval fuzzy time series models, to discuss their efficiency evaluations and accuracy by forecasting daily exchange rate for US Dollars to NT Dollars.
Reference:	[1] Kunhuang, H. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems. 123, 387~394 [2] Wu, B. and Hung, S. (1999). A fuzzy identification procedure for nonlinear time series with example on ARCH and bilinear models. Fuzzy Sets and Systems. 108, 275-287 [3] Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems. 81, 311-319. [4] Zadeh, L. A. (1965). Fuzzy Sets. Information and Control. 8, 338-353. [5] Song, Q. and Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series – part I. Fuzzy Sets and Systems. 54, 1-9. [6] Chen, S. M. (2002). Forecasting enrollments based on high order fuzzy time series. Cybernetics and Systems. 33, 1-16. [7] Tseng, F. M. and Tzeng G. H. (1999). Fuzzy Seasonal Time Series for Forecasting the Production Value of the Mechanical Industry in Taiwan. Technological Forecasting and Social Change. 60. 263–270. [8] Chiang, D. and Wang, Y. (2000). Mining time series data by a fuzzy linguistic summary system. Fuzzy Sets and Systems. 112, 419-432. [9] Miller, G. A. (1956). The magical number seven plus or minus two: Some limits on our capacity of processing information. The Psychological Review. 63(2), 81–97. [10] Hsu, H. L. (2008). Evaluating forecasting performance for interval data. Computers and Mathematics with Applications. 56, 2155-2163. [11] 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。 [12] 曾淑惠 (2004) 多變量模糊時間數列模式之應用以台灣地區高職教師人數之預測為例。教育與心理研究, 第 27 卷, 第四期, 頁845-861。 [13] 吳柏林 (2005) 模糊統計導論方法與應用。五南出版社。 [14] 吳柏林 (1995) 時間數列分析導論。華泰書局。
Description:	碩士國立政治大學應用數學研究所 96751014 99
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0096751014
Data Type:	thesis
Appears in Collections:	[Department of Mathematical Sciences] Theses

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