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研究生:李承勳
研究生(外文):Chen-Hsun Li
論文名稱:以權重轉移矩陣為基礎的模糊時間序列預測門診人數
論文名稱(外文):Fuzzy Time Series based on weighted-transitional matrix for forecasting outpatient visits
指導教授:鄭景俗鄭景俗引用關係
指導教授(外文):Ching-Su Chen
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:61
中文關鍵詞:模糊時間序列門診人數模糊權重轉移矩陣
外文關鍵詞:Fuzzy time seriesthe number of outpatient visitsfuzzy weighttransition matrix
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預測活動在我們日常生活中一直扮演著極重要的角色。傳統的時間序列方法能解大部分的預測問題,但是對於預測語意值形式的歷史資料卻愛莫能助;再者其需要較多的歷史資料,且要確保資料呈現常態分配才能使預測結果更加準確。為了解決這類的預測問題,模糊時間序列因此誕生!
然而,在模糊時間序列的世界中,仍然可以察覺到一個問題的存在,在之前的相關研究領域中,其目光總是放在明顯語意值的擷取,而忽略了背後可能存在著極具關鍵性的隱藏語意值,使得建立的預測推論規則並不是非常周延!在本篇論文中,為了面對未來的多變性,提出了一個能廣泛記錄線索的模式,此模式用了一個新的概念:權重轉移矩陣,並將其加入兩個新的預測方法:「歸屬選擇法」和「期望值法」,進而將模糊輸出值有效地轉換成我們想要的預測結果。
接著,利用阿拉巴馬每年的新生入學人數來衡量此新模式的效能,並與其他相關研究做一比較;結果顯示,本研究模式有較高的預測準確率。
最後,透過某醫院內科門診人數的歷史資料,來驗證本篇研究所提的模式。從醫務管理的角度上來看,若可以有效的預測門診人數,對於醫院內的營運管理、人事安排、資源分配…等,便能提供比較有利的資訊來幫助醫管人員運籌帷幄,進而提升醫療上的服務品質與醫院本身的整體競爭力!
It is obvious that forecasting activities play an important role in our daily life. Traditional time series methods can predict the trend problem, but fail to forecast the problems with linguistic historical data. Furthermore, traditional time series method requires more history data and the data must be normal distribution to get a better forecasting performance. To deal with these kinds of problems, fuzzy time series is proposed.

There are still existed a drawback which can be detected: In the world of fuzzy time series, the view always focus on obvious linguistic values and ignores slight but maybe crucial clues behind those obvious one in forecasting process. In this thesis, some comprehensive methods are proposed for promoting performance and facing the changeable trend in the future. We use a new concept called weighted-transitional matrix adding into two new methods for translating fuzzified outputs to regular number, one is Expectation Method and the other is Grade-Selection Method.

Next, the yearly data on enrollments at the University of Alabama were used to evaluate the performance of the proposed methods. The forecasting accuracy of the proposed methods is better than other methods’.

Finally, a practical verification is accomplished. We choose the number of outpatient visits as our data set just because if we could forecast the number of outpatient visits more exactly, it would help hospital effectively manage operation, distribute resource, and so on.
摘要 i
Abstract ii
致謝 iv
Content v
List of Figures vi
List of Tables vii
1. Introduction 1
1-1. Background and Motivation 1
1-2. Research Objectives 2
1-3. Research Limitations 2
1-4. Organization of the thesis 2
2. Literature Review 4
2-1. Fuzzy Set Theory 4
2-1-1. Fuzzy Set and Fuzzy Number 4
2-1-2. Linguistic Variable and Linguistic Value 6
2-1-3. Defuzzification 6
2-2. Fuzzy Time Series 7
2-3. Importance of the number of outpatient visits 14
3. The Methodology 15
3-1. Research Framework 15
3-2. The Algorithm of Proposed Method 18
3-3. Numerical example 28
3-4. Comparison 34
4. Verification 36
4-1. Partition and Fuzzify Data Set 37
4-3. Defuzzify and Forecast 40
4-4. Compare Results 42
4-4-1 Performance of Adjusting Linguistic Values 43
4-4-2 Performance of Grade Selection 44
4-4-3 Comparing Various Forecasting Output Methods 44
4-4-4 Discussions and Findings 45
5. Conclusions and Future Works 49
Reference 51
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