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Author: 高培元
Pei-yuan Kao
Thesis Title: 根據模糊時間序列、粒子群最佳化技術及支援向量機以預測台灣加權股價指數之新方法
Forecasting the TAIEX Based on Fuzzy Time Series, Particle Swarm Optimization Techniques and Support Vector Machines
Advisor: 陳錫明
Shyi-ming Chen
Committee: 呂永和
Yung-ho Leu
李惠明
Huey-ming Lee
李立偉
Li-wei Lee
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2012
Graduation Academic Year: 100
Language: 英文
Pages: 59
Keywords (in Chinese): 模糊集合模糊時間序列支援向量機台灣加權股價指數
Keywords (in other languages): fuzzy sets, fuzzy time series, support vector machines, TAIEX
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  • 近年來,模糊時間序列成功被應用在處理預測問題,從過去的時間序列歷史資料中,我們可以發現每筆資料變化量的斜率是一個重要的訊息,由此可發現此時間序列資料具有某種變化的趨勢,進一步,我們可以利用這個趨勢來預測出未來的變化值。
    本論文旨在根據模糊時間序列、粒子群最佳化技術及支援向量機提出一個預測台灣加權股價指數之新方法。在此方法中,我們先計算出每天台灣加權股價指數之變化量的斜率,再利用粒子群最佳化技術找出最佳的區間、接著我們模糊化時間序列資料的斜率並產生模糊邏輯關係,再組成模糊邏輯關係群。然後,我們把預測問題轉換成分類問題,並且用支援向量機做分類,分類出來的模糊集合將之解模糊化後產生一個預測值,以得到下一個交易日的加權指數預測值。實驗結果顯示本論文所提的方法比目前已存在的方法具有更高的預測準確率。


    In recent years, fuzzy time series has successfully been used to deal with forecasting problems. From the historical time series data, we can see that the slope of variation of each datum is an important to find the trend of fuzzy time series, where we can use the trend to forecast the variation of future.
    In this thesis, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series, particle swarm optimization and support vector machines. In the proposed method, we first calculate the slope of change of the daily Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), and use particle swarm optimization techniques to find optimal intervals in the universe of discourse. Then, we fuzzify the slope of the time series data and generate fuzzy logical relationships to construct fuzzy logical relationship groups. Then, we transfer the forecasting problem into the classification problem and use the support vector machine to deal with the classification. The classified result using the support vector machine is a fuzzy set. Then, we defuzzify the fuzzy set to get the forecasting value of the next trading day. The experimental results show that the proposed method outperforms the existing methods.

    Abstract in Chinese.............................................i Abstract in English............................................ii Acknowledgements..............................................iii Contents.......................................................iv List of Figures and Tables.....................................vi 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.....................................4 2.1 Basic Concepts of Fuzzy Sets................................4 2.2 Summary.....................................................5 Chapter 3 Fuzzy Time Series....................................6 3.1 Basic Concepts of Fuzzy Time Series.........................6 3.2 Summary.....................................................8 Chapter 4 Particle Swarm Optimization..........................9 4.1 Basic Concepts of Particle Swarm Optimization...............9 4.2 Summary....................................................10 Chapter 5 Support Vector Machines.............................11 5.1 Basic Concepts of Support Vector Machines..................11 5.2 Soft Margin................................................14 5.3 Nonlinear Classification...................................16 5.4 Summary....................................................17 Chapter 6 Forecasting the TAIEX Based on Fuzzy Time Series, Particle Swarm Optimization Techniques and Support Vector Machines...............18 6.1 A New Method for Forecasting the TAIEX Based on Fuzzy Time Series, Particle Swarm Optimization techniques and Support Vector Machines ...............................................18 6.2 Experimental Results.......................................40 6.3 Summary....................................................43 Chapter 7 Conclusions.........................................44 7.1 Contributions of This Thesis...............................44 7.2 Future Research............................................45 References.....................................................46

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