時間序列預測已經被廣泛的應用於許多領域，逐漸受到重視。近來許多人工智慧技術已成功地應用於預測技術。相較於傳統統計方法，人工智慧技術更適合應用在真實資料，預測效能也優於統計方法。因此，我們提出一個結合kNN、基因演算法與模糊類神經網路的時間序列預測系統。該系統不只可應用於單步預測，更可拓展到多步預測。
傳統的模型建立方式是以一個完整的訓練資料集產生一個預測模型，並以此模型進行預測。有別於傳統的模型建立方式，我們使用kNN從訓練資料集中挑選出與輸入資料相似的資料作為訓練資料集，因此針對每筆輸入資料皆產生不同的預測模型。藉由kNN可有效縮短訓練時間、降低預測誤差。
時間序列預測是藉由過去的歷史資訊建立輸入與輸出的關係產生預測結果，大多數的時間序列預測方法僅選取鄰近時間點的資料作為輸入，但不同的資料具有不同的週期，並非所有的資料皆適用這種方式，因此我們選擇以基因演算法來挑選適當的歷史資訊，最佳化單步預測的效能。系統模型採用回授式的模糊類神經網路，將預測步進由單步延伸到多步預測。我們提出一個新的回授式架構的模型訓練方式，此方法可以同時最小化每筆預測預測誤差，降低誤差擴散的情形。
最後我們以幾個常用的效能評估資料來比較我們的方法與其他方法的預測能力，由實驗可得知我們的方法可以提供更準確地預測。

Time series prediction have been used in many fields. Forecasting the future behavior of a real-world time series data is an important research and application area.
Recently, many soft computing methods have been proposed for time series forecasting. In this paper, a kNN based neuro-fuzzy multi steps ahead predictor is developed for time series prediction.
In general, we use all historical data to create a global model. Instead of using all training data to training a model, we utilize the kNN method to dynamically select k most similar data as the training set to create a local model at each prediction. In this way, the training set each time is more relevant to the prediction to be done than the global model approach.
Many method use nearby historical data as input. We use genetic algorithm to choose optimal input for neuro-fuzzy system and optima one step ahead prediction.
We use a recurrent neuro-fuzzy system to generate prediction results, training a one step ahead prediction model, and iterates it by taking the predicted values as a part of the input. In this paper, we propose a new training method to minimum prediction error for each prediction.
Experimental results have shown that our approach can provide more accurate predictions than other methods.

致謝 i
摘要 ii
Abstract iii
圖目錄 vii
表目錄 viii
第一章 導論 1
1.1. 研究背景與目的 1
1.2. 論文架構 2
第二章 文獻探討 3
2.1. 時間序列 3
2.2. 單步預測 3
2.2.1. ARMA 3
2.2.2. 類神經網路 4
2.2.3. SVM 5
2.2.4. 多元迴歸分析 7
2.3. 多步預測 8
2.3.1. 直接預測 8
2.3.2. 遞迴式預測 8
第三章 研究方法 10
3.1. 系統架構 10
3.2. 基因演算法 12
3.2.1. 編碼與族群初始化 14
3.2.2. 選擇 15
3.2.3. 突變 16
3.2.4. 交配 16
3.3. 局部模型 17
3.4. 單步預測 19
3.4.1. 模糊類神經網路架構 19
3.4.2. 混合式學習演算法 22
3.5. 多步預測 25
第四章 實驗結果與分析 29
4.1. 單步預測 29
4.1.1. 台灣加權指數預測 29
4.1.2. 波蘭電力負載預測 34
4.2. 多步預測 36
4.2.1. 雷射強度預測 36
4.2.2. ENUITE電力負載預測 38
4.3. 討論 40
第五章 結論與未來展望 45
5.1. 結論 45
5.2. 未來研究方向 45
參考文獻 46

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