根據自動分群演算法及模糊時間序列以處理預測問題之新方法

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研究生：	王乃毅 Nai-yi Wang
論文名稱：	根據自動分群演算法及模糊時間序列以處理預測問題之新方法 New Methods for Handling Forecasting Problems Based on Automatic Clustering Techniques and Fuzzy Time Series
指導教授：	陳錫明 Shyi-ming Chen
口試委員:	何正信 Cheng-seen Ho 陳榮靜 Rung-ching Chen 李惠明 Huey-ming Lee 呂永和 Yung-ho Leu
學位類別：	碩士 Master
系所名稱：	電資學院 - 資訊工程系 Department of Computer Science and Information Engineering
論文出版年：	2008
畢業學年度：	96
語文別：	英文
論文頁數：	71
中文關鍵詞：	自動分群演算法、模糊集合、模糊時間序列、模糊預測、模糊邏輯關係、模糊趨勢關係
外文關鍵詞：	Automatic clustering techniques, Fuzzy forecasting, Fuzzy logical relationships; Fuzzy-trend logical
相關次數：	點閱：295 下載：6
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模糊時間序列已經被用來處理很許多預測問題，例如：學生註冊人數的預測、溫度預測、貨物需求量預測、股票預測等。目前已存在之模糊時間序列預測的方法之目標均在追求更高的預測準確率。只要我們能預測的愈準確，那我們就可以從中得到更多的益處。
本論文根據自動分群演算法及模糊時間序列提出三個新方法以處理預測問題。在本論文的第一個方法中，我們以自動分群演算法為基礎，提出了一個不用預先決定論述宇集之區間個數的自動區間產生演算法。我們結合模糊時間序列及自動區間產生演算法以預測美國阿拉巴馬大學的學生註冊人數。在本論文的第二個方法中，我們結合了兩因子高階模糊時間序列(Two-Factor High Order Fuzzy Time Series)及自動區間產生演算法，提出一個新方法以做溫度預測及期貨指數預測。此方法是根據歷史資料以建立兩因子高階模糊邏輯關係(Two-Factors High-Order Fuzzy Logical Relationship)，並利用自動區間產生演算法以使歷史資料的論述宇集產生不同長度的區間，以提高預測準確率。在第三個方法中，我們提出模糊趨勢邏輯關係(Fuzzy-Trend Logical Relationship)的概念，並利用自動區間產生演算法以調整論述宇集中各區間之大小來處理預測問題，以提高預測準確率。本論文所提之三個新方法均比目前已存在之方法具有更高的預測準確率。

Fuzzy time series have been used to handle prediction problems, such as the enrollments prediction, the temperature prediction, the inventory prediction, the stock index prediction, …, etc. The goal of the existing fuzzy time series forecast methods is to get a higher forecasting accuracy rate. If we can get a higher forecasting accuracy rate, then we get more benefits.
In this thesis, we present three new methods to handle forecasting problems based on automatic clustering algorithms and high-order fuzzy time series. In the first method, we present an automatic clustering algorithm to automatically generate intervals based on automatic clustering techniques, where the proposed algorithm does not need to predefine the number of intervals in the universe of discourse. In the first method, we apply the proposed automatic clustering algorithm and fuzzy time series to forecast the enrollments of the University of Alabama. In the second method, we present a method for temperature prediction and the TAIFEX forecasting based on the proposed automatic clustering algorithm and two-factors high-order fuzzy time series. The proposed method constructs two-factors high-order fuzzy logical relationship based on the historical data and uses the proposed automatic clustering algorithm to produce different length of intervals in the universe of discourse for temperature prediction and the TAIFEX forecasting to increase the forecasting accuracy rate. In the third method, we present a new method to handle forecasting problems, where the proposed method divides fuzzy logical relationships into fuzzy-trend logical relationship groups based on the trend of the adjacent fuzzy sets in the fuzzy logical relationships. The proposed method gets a higher average forecasting accuracy rate. In summary, the proposed three methods get higher forecasting accuracy rates than the existing methods.

Abstract in Chinese i
Abstract in English iii
Acknowledgements v
Contents vi
List of Figures and Tables viii
Chapter 1  introduction 1
1.1  Motivation 1
1.2  Related Literature 2
1.3  Organization of This Thesis 3
Chapter 2  Fuzzy Set Theory and Fuzzy Time Series 5
2.1  Basic Concepts of Fuzzy Sets 5
2.2  Fuzzy Time Series 11
2.3  Summary 13
Chapter 3  An Automatic Clustering Algorithm 14
3.1  An Automatic Clustering Algorithm [7] 14
3.2  An Automatic Clustering Algorithm for Generating Intervals from the Numerical Data 15
3.3  Summary 22
Chapter 4  Forecasting Enrollments Based on Automatic Clustering Techniques and Fuzzy Time Series 23
4.1  A New Method for Forecasting Enrollments Based on the Proposed Automatic Clustering Algorithm and Fuzzy Time Series 23
4.2  Experimental Results 32
4.3  Summary 33
Chapter 5  Temperature Prediction and TAIFEX Forecasting Based on Automatic Clustering Techniques and Two-Factors High-Order Fuzzy Time Series 34
5.1  A New Forecasting Method Based on the Proposed Automatic Clustering Techniques and Two-Factors High-Order Fuzzy Time Series 34
5.2  Experimental Results 50
5.3  Summary 53
Chapter 6 Handling Forecasting Problems Based on High-Order Fuzzy Time Series and Fuzzy-Trend Logical Relationship Groups 54
      6.1  A New Method for Forecasting the TAIEX Based on High-Order Fuzzy Time Series and Fuzzy-Trend Logical Relationship Groups 54
6.2  Experimental Results .67
6.3  Summary 72
Chapter 7  Conclusions 73
7.1  The Contributions of This Thesis 73
7.2  Future Research 74
References 76

                                

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