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研究生： 黃燿麟
Yao-Lin Huang
論文名稱： 應用於模糊時間序列之混合預測模型
Hybrid Forecasting Models for Fuzzy Time Series
指導教授： 古鴻炎
Hung-Yan Gu
洪西進
Shi-Jinn Horng
口試委員: 楊昌彪
Chang-Biau Yang
許健平
Jang-Ping Sheu
吳金雄
Chin-Hsiung Wu
黎碧煌
Bih-Hwang Lee
鍾國亮
Kuo-Liang Chung
陳秋華
Chyou-hwa Chen
學位類別： 博士
Doctor
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： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 98
中文關鍵詞： 加權模糊關係矩陣 、聚合模糊資訊 、粒子群最佳化 、模糊時間序列 、混合式預測模型 、經驗法則調適
外文關鍵詞： Aggregated Fuzzy Information, Particle Swarm Optimization, Fuzzy Time Series, Hybrid Forecasting Model, Weighted Fuzzy Relationship Matrix, Heuristic Adaptation
相關次數： 點閱：233  下載：10 
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    • 時間序列係指以時間順序型態出現之一連串觀測值集合，而時間序列預測乃是探討此一觀測值集合的關係，並由此關係預測未來。傳統的時間序列預測模型通常需要較嚴格的基本假設，使得預測模型的建構較為困難；而模糊時間序列預測模型並不需要嚴格的基本假設並且可以處理非數值性資料，使得預測模型的建構較為簡單與更具彈性。在模糊預測方法中，分割區間的大小與模糊關係的規則為影響模糊預測模型準確率的兩個主要因素，可使用最佳化演算法調整此二因素到最佳值。在生物智能演算法中，粒子群最佳化和基因演算法的求解能力大致相同，但粒子群最佳化演算法的計算效率高於基因演算法。因此，我們將問題設定在資料量少與變動性大的時間序列與使用基於模糊時間序列結合粒子群最佳化預測模型，以減少模型的基本假設，增加模型的通用性以及提高預測的準確率。
    基於模糊理論與粒子群最佳化演算法，在本論文中建構了三種混合式模糊時間序列預測模型，分別為全域性資訊聚合區域性資訊預測模型、加權模糊關係矩陣預測模型和經驗法則可調適預測模型。
    預測模型一，探討模糊時間序列之全域性資訊聚合區域性資訊預測模型。藉由所有歷史資料建立的模糊關係可獲得全域性資訊，產生主要預測值；區域性資訊則受到最近歷史資料的波動影響，用以微調主要預測值，再利用粒子群最佳化演算法調整區間，進行變動區間模糊預測，求得最佳預測值。
    預測模型二，探討加權模糊關係矩陣應用於模糊時間序列資料之預測模型。利用模糊關係矩陣可以保留最近歷史資料波動的所有模糊隸屬關係而非單一數值之特性，而加權模糊隸屬關係用以反應最近歷史資料的波動影響，再利用粒子群最佳化演算法調整區間，進行變動區間模糊預測，求得最佳預測值。
    預測模型三，探討經驗法則可調適的方法應用於模糊時間序列資料之預測。首先，計算兩組候選預測值，分別為全域性模糊關係預測值與區域性模糊移動平均預測值，然後根據最近歷史資料波動的趨勢，應用經驗調適法則在兩組候選預測值中選擇一組候選預測值作為預測結果，再利用粒子群最佳化演算法調整區間，進行變動區間模糊預測，求得最佳預測值。
    本論文提出了三種混合式模糊時間序列結合粒子群最佳化演算法之預測模型，為了驗證模型的預測效能，我們使用以往文獻中常用的基準時間序列進行實驗，包括入學人數與旅遊人數。實驗結果證明我們提出的混合式模糊預測模型優於傳統模糊預測模型，其在訓練階段與測試階段的預測結果獲得更高之準確率與穩定性。最後，針對實務上資料量少與變動性大的時間序列預測問題，我們希望此三種混合式模糊時間序列預測模型可以提供決策者高準確率與高穩定性的預測需求。

    A time series is a set of observations of a variable collected over time and ordered chronologically. Time series forecasting is employed to explore the relationship of this collection of observations and to predict the future. Traditional time series forecasting models usually require more stringent assumptions and the construction of forecasting model is difficult. Fuzzy time series forecasting models do not require strict assumptions, and the construction of forecasting models is simpler and more flexible. The lengths of intervals and the fuzzy relation rules are two major factors affecting the accuracy of fuzzy forecasting models. Optimization algorithms can be used to adjust these two factors to the best values. In biological intelligence algorithms, particle swarm optimization and genetic algorithms on average yield the same effectiveness (solution quality), but particle swarm optimization is more computationally efficient than are the genetic algorithms. Therefore, we focus on short-term time series with large fluctuations and combine fuzzy time series with the particle swarm optimization in order to reduce the model's basic assumptions, increase the generality of the model and improve the accuracy of the prediction. In this dissertation, the authors develop three kinds of hybrid forecasting models as follows:
    First, a hybrid forecasting model integrates aggregated fuzzy time series and particle swarm optimization. The global information related to all historical data is used to generate the main forecasted values. The local information related to the recent fluctuations of the historical data is used to fine-tune the main forecasted values. To find the optimal interval partition, particle swarm optimization is used to adjust the interval lengths.
    Second, a hybrid forecasting model which combines the weighted fuzzy relationship matrices and particle swarm optimization algorithm is introduced. The fuzzy relationship matrices are derived from the corresponding fuzzy logical relationships; the weighting scheme then assigns the largest weights to the latest past fuzzy set of a fuzzy relationship matrix to capture the efficient fuzzy relations. The fuzzy relationship matrices can maintain all of the fuzzy membership values of the fuzzy sets rather than a single value of the fuzzy sets. To find the optimal interval partition, particle swarm optimization is applied to adjust the interval lengths.
    Third, a hybrid forecasting model which integrates heuristic fuzzy time series and particle swarm optimization is presented to improve the forecasting accuracy of the time series data. The trend values are used to reflect short-term fluctuations of limited time series data. In each forecast, the heuristic rules automatically adapt the forecasted values based on trend values, and particle swarm optimization is applied to adjust the interval lengths in the universe of discourse for accurate forecasting.
    The proposed hybrid models are applied to well-known benchmarks available in the literature to verify the forecasting performance. The experimental results show that the proposed models outperform other listed models in both the training and testing phases. Finally, the three hybrid forecasting models can provide effective forecasting performance for short-term time series with large fluctuations and offer decision makers with more precise and highly stable forecasting results.

    論文摘要 I Abstract III List of Contents VI List of Tables VIII List of Figures X Chapter 1 Introduction 1 Chapter 2 Related works 6 2.1 Fuzzy time series 6 2.2 Particle swarm optimization 8 Chapter 3 Aggregated fuzzy time series and particle swarm optimization 11 3.1 AFPSO model 11 3.1.1 Fuzzy forecasting by aggregated fuzzy time series 12 3.1.2 Interval partition by PSO method 17 3.2 Empirical study for enrollment forecasting 18 3.2.1 The implementation of the fuzzy forecasting method 19 3.2.2 Interval partition by PSO method 28 3.3 Performance evaluation of AFPSO model 29 3.3.1 Experimental results in the training phase 29 3.3.2 Experimental results in the testing phase 35 Chapter 4 Weighted fuzzy relationship matrix and particle swarm optimization 38 4.1 FMPSO model 38 4.1.1 Fuzzy forecasting by the weighted FRMs 39 4.1.2 Interval partition by PSO method 45 4.2 Empirical study for enrollment forecasting 45 4.2.1 The implementation of the fuzzy forecasting method 45 4.2.2 Interval partition by PSO method 54 4.3 Performance evaluation of FMPSO model 54 4.3.1 Experimental results in the training phase 55 4.3.2 Experimental results in the testing phase 59 Chapter 5 Heuristic fuzzy time series and particle swarm optimization 62 5.1 HFPSO model 63 5.1.1 Fuzzy forecasting by heuristic adaptation 63 5.1.2 Interval partition by PSO method 68 5.2 Empirical study for tourism demand forecasting 69 5.2.1 The implementation of the fuzzy forecasting method 69 5.2.2 Interval partition by PSO method 72 5.3 Performance evaluation of HFPSO model 73 5.3.1 TW2USA forecasting 75 5.3.1.1 Experimental results in the training phase 75 5.3.1.2 Experimental results in the testing phase 76 5.3.2 USA2TW forecasting 78 5.3.2.1 Experimental results in the training phase 78 5.3.2.2 Experimental results in the testing phase 80 Chapter 6 Conclusions 82 Bibliography 85

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