本研究以神經模糊建模的技術來進行股票預測，當蒐集到的預測資料龐大時，有效將資料簡化便成為一項重要工作。所以本研究提出以相似度為基礎的資料縮減演算法，以減少監督式學習的訓練集規模。訓練樣本逐一輸入演算法後，透過相似度測試分組成一群，每一群的統計平均值皆視為代表該群中所有樣本的原型。接著，這些平均值的集合可用來取代原有訓練集，從而縮減日後進行監督式學習時所使用的訓練集。此一方法具有每群中所含資料的分佈獲得統計上的詳細說明，所獲得的每個原型資料都是相應群中所含樣本的良好代表，它會根據原始訓練樣本之間的相似度關係和分佈，自動萃取不同的代表數等優點。此外本研究提出的方法可以有效地同時應用於迴歸問題和分類問題，實驗結果顯示，本研究提出的方法比其他資料縮減方法更有效。
此外，本研究將一組給定的訓練資料為依據，提出第Ⅱ型神經模糊建模技術在股票價格預測上的應用。自我建構式之分群方法可以自動產生第Ⅱ型模糊規則，所得出的第Ⅱ型模糊規則由混合式學習演算法修正，透過輸入相似度測試和輸出相似度測試將給定的訓練資料集分成群集，來自每個群集的第Ⅱ型TSK規則便形成一個模糊規則庫，接著以粒子群優演算法和最小平方估計法來修正與這些規則有關的前鑑部份和後鑑部分參數。執行時取自台灣股票加權指數(TAIEX)和納斯達克(NASDAQ)指數的幾個資料集所得出的實驗結果，證實第Ⅱ型神經模糊系統建模的方法在股票價格預測方面具有成效。

We propose a similarity-based prototype reduction algorithm to reduce the training set size for supervised learning. Training patterns are input to the algorithm one by one and grouped into blobs through similarity tests. The statistical mean of each blob is regarded as a prototype representing all the patterns included in the blob. The collection of such means can then be used to substitute the original training set, and, consequently, the training set for later supervised learning is reduced. This approach has several advantages. The distribution of the data contained in each blob is statistically well described. Each obtained prototype is a good representative of the patterns included in the corresponding blob. Different numbers of representatives are extracted auto- matically according to the similarity relationship among and the distribution of the original training patterns. Furthermore, our method can be applied efficiently to both regression and classification problems. Experimental results show that the proposed method performs more effectively than other prototype reduction methods.
Moreover, we present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base. Then the antecedent and consequent parameters associated with the rules are refined by particle swarm optimization and least squares estimation. Experimental results, obtained by running on several datasets taken from TAIEX and NASDAQ, demonstrate the effectiveness of the type-2 neuro-fuzzy modeling approach in stock price prediction.

中文摘要 Ⅲ
英文摘要 Ⅳ
誌謝 Ⅴ
目錄 Ⅵ
圖目錄 Ⅸ
表目錄 Ⅹ
第 一 章緒論 1
1.1研究背景與動機 1
1.1.1巨量資料之處理 1
1.1.2股票預測 2
1.2研究目的及內容 4
1.2.1資料預處理 4
1.2.2模糊建模技術於股票預測之應用 5
1.3論文架構 5
第 二 章股票預測介紹 6
2.1預測基礎 6
2.2股票基礎常識 8
2.3股票預測方法 9
第 三 章支持向量機與軟式計算 11
3.1支持向量回歸簡介 11
3.1.1支持向量機 11
3.1.2持向量機分類的數學原理 13
3.1.3基於線性規劃的SVM分類 15
3.1.4支援向量回歸SVR模型 16
3.1.5支持向量機分類與支持向量回歸的關係 19
3.2神經模糊系統 20
3.2.1神經網路 20
3.2.2監督式學習網路 21
3.2.3非監督式學習網路 22
3.2.4模糊理論 22
3.2.5神經模糊網路 25
3.3第Ⅱ型模糊系統概述 28
3.3.1第II型模糊集的基本概念 28
3.3.2第II型模糊集的基本運算 29
3.3.3第II型模糊系統組成 30
第 四 章系統建模技術與應用 37
4.1資料處理分析 37
4.1.1問題定義 37
4.1.2相關研究 38
4.1.3資料縮減方法 40
4.1.4實例 47
4.2第Ⅱ型神經模糊系統建模技術 53
4.2.1基本理論 53
4.2.2規則庫建構 57
4.2.3參數修正 62
4.2.4實例 67
第 五 章實驗與結果 70
5.1資料縮減方法 70
5.1.1實驗一:三個分類資料集的比較 70
5.1.2實驗二:兩個迴歸資料集的比較 75
5.1.3實驗三: SBPR不同設定值的影響 76
5.2第Ⅱ型神經模糊建模法對股價的預測 81
5.2.1實驗一:TAIEX資料預測 81
5.2.2實驗二: TAIEX和TAIFEX資料預測 86
5.2.3實驗三: TAIEX、DJTA和NASDAQ資料預測 89
5.2.4實驗四: T2NFS與SVM方法比較 94
5.2.5實驗五:誤差測量 95
第 六 章結論與未來研究方向 97
6.1結論 97
6.2未來研究方向 100
參考文獻 101

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