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研究生:陳宣佑
研究生(外文):Hsuan-YuChen
論文名稱:以穩態機率模型建構多因子模糊時間序列預測之研究
論文名稱(外文):Forecasting of Multiple-factor Fuzzy Time Series with a Steady-State Probabilities Model
指導教授:李昇暾李昇暾引用關係
指導教授(外文):Sheng-Tun Li
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工業與資訊管理學系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:61
中文關鍵詞:模糊時間序列預測馬可夫模型穩態
外文關鍵詞:Fuzzy Time SeriesForecastingMarkovSteady-state
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在這個大數據時代,常會使用到統計或數學模型進行歷史數據的分析,並利用數據分析結果,預測未來的變化及趨勢。在過去的研究中,迴歸分析、類神經網路及羅吉斯迴歸模型,或者是支援向量機等等,都是常被使用的分析方法。
但以上皆是採用精確的數值資料以進行預測,對於約略表示的數值(如天氣資料)或語意變數則無法加以計算。直到1965年Zadeh的模糊理論出現,才能對模糊數加以分析及探討。目前,模糊理論已經被廣泛地運用在許多領域,而相關的模糊模型則包括了模糊時間序列、模糊迴歸等等。
在模糊時間序列預測模型中,常會遇到變數的限制、忽略模糊關係出現的頻率,使得預測結果有較大的誤差,解釋力也顯得不足。因此,本研究將模糊理論結合了馬可夫模型,利用馬可夫矩陣取代傳統的模糊關係矩陣,將遞移關係的頻率納入考量,並且結合了變數獨立等性質,將相關係數加入模糊矩陣中,使其考慮多因子的影響,最後利用模糊馬可夫矩陣計算後的穩態結果進行合併,建構一套新的模糊時間序列預測方法,提升預測結果的準確性。
In this time of big data, many people often using statistical or mathematical models to analyze historical data and use data analysis to predict the changes of future. In past studies, regression analysis, neural network and logistic regression models or support vector machine, etc., are often used to analyze.
But the above methods are based on numerical data to accurately predict, for the fuzzy data (semantic information, such as weather information), these methods cannot be used to analysis. Until 1965, Zadeh proposed the fuzzy theory, the linguistic variable finally can be analyzed and discussion. Currently, the fuzzy theory has been widely used in many fields, such as fuzzy time series, fuzzy regression, etc.
However, there are some problems in the fuzzy time series forecasting models. Like the limits of the numbers of variables, ignoring the frequencies of fuzzy sets. It make the large predictions error and the lack explanation ability. Therefore, in order to solve the above problems, in this study, we combine the fuzzy theory and the Markov theory, using Markov matrix instead of the traditional fuzzy relation matrix, consider the frequency of transfer status. Calculate the steady-state probabilities and use correlation coefficient to combine each variable. Build a whole new predict model and enhance the prediction accuracy of the results.
摘要 I
Abstract II
誌謝 III
Contents IV
List of Tables VI
List of Figures VII
CHAPTER 1 Introduction 1
1.1 Background and motivation 1
1.2 The contribution and the structure of this study 3
CHAPTER 2 Literature Review 5
2.1 Fuzzy set theory 5
2.2 Fuzzy time series 8
2.2.1 Basic definition of fuzzy time series 8
2.2.2 Forecasting model of fuzzy time series 11
2.3 Markov model 21
2.4 Additive smoothing 24
CHAPTER 3 Model Development 25
3.1 Fuzzifying historical data 27
3.2 Calculate the Correlation coefficient 28
3.3 Combined the Markov transition matrix 29
3.4 Forecasting and defuzzification 33
3.5 Demonstration of the proposed model 34
CHAPTER 4 Evaluation and analysis 41
4.1 Evaluation Indicators 41
4.2 One-factor - Enrollment data of Alabama 42
4.2.1 Experimental content 43
4.2.2 Evaluation and analysis 49
4.3 Multi-factor - Weather data of Alishan 51
CHAPTER 5 Conclusion and Future Work 53
5.1 Conclusion 53
5.2 Management Implication 54
5.3 Future Work 55
Reference 56
Appendixes 59
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