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姓名	李明杰(Ming-Chieh Lee)  查詢紙本館藏	畢業系所	資訊管理學系在職專班
論文名稱	探討應用資料增強及改善類別不平衡問題 對不同股票圖形之影響-以台灣股市0050ETF為例 (Using Time-series Data Augmentation and Handling Class Imbalance Issues with Different Image Representations to Predict Stock Trends)
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2028-7-1以後開放)
摘要(中)	預測股票趨勢對於市場參與者是一個重要的問題，因為即使預測準確性的微小改進也可能導致比其他人有更好的交易決策。且在深度學習技術的發展下，有愈來愈多研究將結構化股價資訊或二維圖像運用於股價預測中，過去的研究將股價資訊轉換成圖像的方式有很多種，包括直接將一維結構化股價資訊直接轉換(reshape)成二維結構化圖像，或是使用格拉姆角場(Gramian angular field, GAF)技術將一維時間序列轉換成二維圖像，又或者是股價K線圖，但大部分研究僅使用其中一個方法，鮮少有針對同一資料集使用三種圖像表現方式進行分析比較，也較少針對類別不平衡(Class Imbalanced)及時間序列型資料增強(Data Augmentation)這兩個議題進行探討；因此在本研究中，會將台灣股市的歷史股價資訊轉換成三種圖像資料，並改善類別不平衡及使用時間序列型的資料增強方法，實驗結果證實使用上述兩種方法後，在三個圖像表現方式上皆能獲得改善，且K線圖和GAF圖像比二維結構化圖像有更好的分類表現。除此之外，本研究也進一步採用多頭CNN(Multi-head CNN)架構，將GAF圖像輸入進多頭CNN模型，實驗結果證實在台灣股市資料集上多頭CNN比CNN有更好的預測效果。
摘要(英)	Predicting stock trends is an important issue for market participants, as even slight improvements in prediction accuracy can lead to better trading decisions than others. With the development of deep learning techniques, an increasing number of studies are applying structured stock price information or two-dimensional images to stock price predictions. In the past, there have been various methods to transform stock price information into images, including directly converting one-dimensional structured stock price information into two-dimensional structured images, using Gramian Angular Field (GAF) techniques to convert one-dimensional time series into two-dimensional images, or stock price candlestick charts. However, most researchs only use one of these methods, and there is little analysis comparing the three image representation methods on the same dataset. There is also less discussion on the issues of class imbalance and data augmentation for time series data. In this study, historical stock price information from the Taiwan stock market is converted into three types of image data, addressing class imbalance and using time-series data augmentation methods. Experimental results show that both methods improve performance for all three image representations, with candlestick charts and GAF images outperforming two-dimensional structured images in classification performance. In addition, this study further adopts a multi-head CNN (Multi-head Convolutional Neural Network) architecture, inputting GAF images into the multi-head CNN model. Experimental results confirm that multi-head CNN has better prediction performance than CNN on the Taiwan stock market dataset.
關鍵字(中)	★ 股票預測 ★ 深度學習 ★ 圖像分類 ★ 類別不平衡 ★ 資料增強	關鍵字(英)	★ Stock Prediction ★ Deep Learning ★ Image Classification ★ Class Imbalance ★ Data Augmentation
論文目次	誌謝 i 摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 viii 1 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 5 1.4 研究架構 5 2 第二章 文獻探討 7 2.1 一維時間序列二維圖像化 7 2.1.1 二維結構化圖像 7 2.1.2 格拉姆角場(Gramian Angular Field, GAF) 8 2.1.3 K線圖(Candlestick Chart) 9 2.2 類別不平衡(Class Imbalance) 11 2.3 時間序列型資料增強方法 12 2.3.1 視窗切片法(Window slicing) 13 2.3.2 視窗扭曲法(Window warping) 14 2.3.3 時間扭曲法(Time warping) 15 2.3.4 SPAWNER(Sub-optimal Warped Time Series GeneratOR) 16 2.4 在股票市場運用卷積神經網路之相關研究 18 2.5 多頭卷積神經網路 20 3 第三章 研究方法 22 3.1 實驗一架構 22 3.1.1 實驗1.1 不處理類別不平衡也不使用資料增強 23 3.1.2 實驗1.2 處理類別不平衡 33 3.1.3 實驗1.3 應用資料增強 37 3.2 實驗二 使用分群技術和特徵降維的多頭卷積神經網路 41 4 第四章 實驗結果 44 4.1 資料描述 44 4.2 實驗1結果 47 4.2.1 實驗1.1 不處理類別不平衡也不使用資料增強 47 4.2.2 實驗1.2 處理類別不平衡 51 4.2.3 實驗1.3 處理類別不平衡及應用資料增強技術 56 4.2.4 實驗1小結 58 4.3 實驗2結果-使用分群技術和特徵降維的多頭卷積神經網路 59 4.3.1 實驗2小結 61 5 第五章 結論 62 5.1 總結與貢獻 62 5.2 研究限制 63 5.3 未來研究方向與建議 63 參考文獻 65
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指導教授	蔡志豐 周恩頤(Chih-Feng Tsai En-Yi Zhou)	審核日期	2023-6-19
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