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研究生： 王端輝
Wang, Duan-hui
論文名稱： 平滑式隱藏馬可夫模型之二因子模糊時間序列預測模式
Fuzzy Time Series Forcasting based on Smoothing Hidden Markov Model
指導教授： 李昇暾
Li, Sheng-tun
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理研究所
Institute of Information Management
論文出版年： 2009
畢業學年度： 97
語文別： 英文
論文頁數： 47
中文關鍵詞： 模糊集合 、模糊時間序列 、模糊關係 、隱馬可夫 、預測 、平滑技術
外文關鍵詞： Forecasting, Smoothing, Hidden Markov Model, Fuzzy time series, Fuzzy sets
相關次數： 點閱：90  下載：1 
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    • 當今資訊科技發展快速，如何藉由精確的計算何資訊分析來對資料進行預測成為相當重要的議題，但在我們的日常生活中，模糊以及不完全的資料充斥在我們的身邊。因此，近年來，在不確定的環境下模糊時間序列預測漸漸地扮演著越來越重要的角色。本研究擬提出以隱馬可夫模形為基礎之模糊時間序列預測模式，配合平滑技術的應用，預期得到更佳的預測準確率，並利用加權平均法解模糊化，已使預測結果更趨近於真實情況。本研究將把所提出的預測模式實際應用於台北地區1992年~1996年每日平均溫度與雲量資料，藉此以完整說明本研究所提出之預測流程，並透過均方誤與平均預測誤差率此二衡量指標，說明本研究所提出之預測模式較過去相關的模糊時間序列預測模式，擁有較低的預測誤差率。本研究也對2004年~2006年台灣加權股票指數與美國納斯達克指數進行預測，預測結果也顯示本研究能獲得比其他模式更精確的預測準確度。

    Nowadays the information technology is growing fast, and the issue of how to predict through scientific computation and information analysis becomes crucial. But vague and incomplete data described as linguistic variables massively exists in our daily life. Traditional forecasting cannot solve forecasting problems when the historical data are linguistic values, so fuzzy time series forecasting become more and more important. In this paper, we proposed a new fuzzy time series forecasting model based on hidden Markov model using the smoothing approach to acquire more accurate result, with the weighted average defuzzify achieve the true performance of the model approximately. For performance evaluation, the data of daily average temperature and average cloud density from June to September, 1992 to 1996 in Taipei. The experiments validate the better accuracy of the proposed model achieved over traditional fuzzy time series models.

    Abstract I Table of Contents II List of Tables III List of Figures IV Chapter I Introduction 1 1.1 Background and motivation 1 1.2 The goal and contribution of thesis 2 1.3 Thesis organization 3 1.4 The limitation of thesis 4 Chapter II Literature Review 6 2.1 Reviews of fuzzy time series models 6 2.2 Summary 10 Chapter III Model Development 12 3.1 Related Methods 12 3.1.1 Fuzzy Time Series 12 3.1.2 Hidden Markov model 12 3.1.3 HMM based fuzzy time series forecasting 14 3.2 Smoothing techniques 15 3.2.1 Laplace smoothing 16 3.2.2 Backoff smoothing (Katz smoothing) 16 3.2.3 Jelinek-Mercer smoothing 17 3.2.4 Proposed smoothing techniques 17 3.3 The framework of Smoothing Hidden Markov Model 21 Chapter IV Experiment and Evaluation 28 4.1 Experiment on HMM based model 28 4.2 Performance evaluation and comparison 36 4.2.1 Forecasting accuracy 38 4.2.2 Comparisons of different smoothing techniques 40 4.3 Taiwan Weighted Stock Index Experiment 40 4.4 Discussion 41 Chapter V Conclusions and Future Work 43 5.1 Conclusions 43 5.2 Future Work 44 References 45

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