卷積模糊神經系統於財金市場數據之研究

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姓名

許敦盛(Dun-Sheng Hsu) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

卷積模糊神經系統於財金市場數據之研究
(CNN Neural Fuzzy Time Series Forecasting in Financial Markets)

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至系統瀏覽論文 (2024-7-15以後開放)

摘要(中)

財金市場的時間序列預測是個具挑戰性的議題。在過去的研究中，多數方法只針對單一目標進行預測。因此，本研究提出多目標預測方法，結合卷積神經網路（Convolutional neural networks, CNN）與球型複數模糊神經系統（Sphere complex neural fuzzy system, SCNFS）。CNN能從模型輸入中，擷取出有用的資訊，藉此提升模型的預測表現；球型複數模糊集（Sphere complex fuzzy sets, SCFSs）能產生多個複數型態的歸屬程度，此特性使本研究所提之模型擁有進行多目標預測的能力。在前處理階段，使用多目標特徵選取，從輸入資料中挑選有影響力之特徵作為模型輸入。參數訓練方面，使用高斯分布型鯨群最佳化演算法（Gaussian distribution based whale optimization algorithm, GD-WOA）及遞迴最小平方估計法（Recursive least squares estimator, RLSE）之複合式最佳化演算法，透過分而治之的方式，GD-WOA針對CNN卷積核參數及球型複數模糊集參數進行訓練；RLSE針對SCNFS之後鑑部層參數進行訓練。為驗證本研究所提之方法，三個實驗以股價指數數據作為資料集，進行不同目標數的時間序列預測。實驗結果與過往文獻相比，有較佳的預測結果，顯示本研究所提之方法，在時間序列預測上有良好效能。

摘要(英)

Time series prediction of financial markets is a challenging issue. In literature, most prediction methods only predict for a single target at a time. In this study, a novel method for multi-target prediction is proposed, integrating a convolutional neural networks (CNN) and a sphere complex neural fuzzy system (SCNFS), called CNN-SCNFS. The membership degree of sphere complex fuzzy sets (SCFSs) can accommodate more membership information than the conventional fuzzy sets, enabling the SCNFS prediction model to achieve multi-output property. In the pre-processing stage, cross-target feature selection is used. However, for the SCNFS, the increasing number of input features usually result in the addition of computational resources on training. To receive the more number of informative features, a CNN architecture is placed in front of the SCNFS. Due to capability of capturing sudden changes, CNN is a suitable model for the chaotic situation. In terms of optimization algorithm, the concept of divide-and-conquer is applied. Gaussian distribution based whale optimization algorithm (GD-WOA) tunes the kernel maps of CNN and the parameters of SCFSs; recursive least squares estimator (RLSE) tunes Takagi-Sugeno layer of SCNFS. Great performance is shown by the GD-WOA-RLSE hybrid optimization algorithm. The trading information of different stock index is used as the data sets, to perform the experiment of multi-target prediction. The experimental results indicate that the proposed method is comparable with the other compared methods on the time series prediction.

關鍵字(中)

★ 多目標時間序列預測
★ 卷積神經網路
★ 模糊神經系統
★ 球型複數模糊集
★ 複合式最佳化演算法

關鍵字(英)

★ multi-targets time series prediction
★ convolution neural networks
★ neural fuzzy system
★ sphere complex fuzzy sets
★ hybrid optimization algorithm

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
符號與專有名詞說明 viii
第一章　緒論 1
1.1 研究背景與目的 1
1.2 研究方法概述 2
1.3 論文架構 2
第二章　文獻探討 3
2.1 特徵選取 3
2.2 複數模糊集 4
2.3 類神經網路 4
2.3.1 卷積神經網路 4
2.3.2 自適應類神經模糊推論系統 5
2.4 最佳化演算法 6
第三章　系統設計與架構 7
3.1 球型複數模糊集 7
3.2 多目標特徵選取 9
3.3 CNN-SCNFS預測模型 12
3.3.1 CNN架構 12
3.3.2 SCNFS架構 13
3.4 複合式最佳化演算法 16
3.4.1 高斯分布型鯨群最佳化演算法 16
3.4.2 遞迴最小平方估計法 18
第四章　實驗實作與結果 20
4.1 實驗資料 20
4.2 實驗評估指標 21
4.3 實驗設定與結果 22
4.3.1 實驗一：單目標股價指數預測 22
4.3.2 實驗二：雙目標股價指數預測 26
4.3.3 實驗三：四目標股價指數預測 29
第五章　實驗結果討論 37
第六章　結論 39
6.1 結論與貢獻 39
6.2 未來研究方向 39
參考文獻 41

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指導教授

李俊賢(Chunshien Li)

審核日期

2019-7-16

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