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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
參考文獻	Abarbanell, J. S., & Bushee, B. J. (1997). Fundamental Analysis, Future Earnings, and Stock Prices. Journal of Accounting Research, 35(1), 1–24. Ahmad, W., Kazmi, B. M., & Ali, H. (2019). Human Activity Recognition using Multi-Head CNN followed by LSTM. 2019 15th International Conference on Emerging Technologies (ICET), 1–6. Akşehir, Z. D., & Kiliç, E. (2022). How to Handle Data Imbalance and Feature Selection Problems in CNN-Based Stock Price Forecasting. IEEE Access, 10, 31297–31305. Akşehir, Z. D., & Kılıç, E. (2022). The Effect of Statistical Attributes on the Determination of Stock Trading Actions. 2022 7th International Conference on Computer Science and Engineering (UBMK), 13–18. Barra, S., Carta, S. M., Corriga, A., Podda, A. S., & Recupero, D. R. (2020). Deep learning and time series-to-image encoding for financial forecasting. IEEE/CAA Journal of Automatica Sinica, 7(3), 683–692. Canizo, M., Triguero, I., Conde, A., & Onieva, E. (2019). Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study. Neurocomputing, 363, 246–260. Chandar, S. K. (2022). Convolutional neural network for stock trading using technical indicators. Automated Software Engineering, 29(1), 16. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357. Chen, Y.-C., & Huang, W.-C. (2021). Constructing a stock-price forecast CNN model with gold and crude oil indicators. Applied Soft Computing, 112, 107760. Fawzi, A., Samulowitz, H., Turaga, D., & Frossard, P. (2016). Adaptive data augmentation for image classification. 2016 IEEE International Conference on Image Processing (ICIP), 3688–3692. Fons, E., Dawson, P., Zeng, X., Keane, J., & Iosifidis, A. (2020). Evaluating data augmentation for financial time series classification (arXiv:2010.15111). arXiv. http://arxiv.org/abs/2010.15111 G. L. Morris. (2006). Candlestick Charting Explained: Timeless Techniques for Trading Stocks and Sutures. McGraw Hill. Gudelek, M. U., Boluk, S. A., & Ozbayoglu, A. M. (2017). A deep learning based stock trading model with 2-D CNN trend detection. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. Guennec, A. L., Malinowski, S., & Tavenard, R. (2016, September 19). Data Augmentation for Time Series Classification using Convolutional Neural Networks. ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data. https://shs.hal.science/halshs-01357973 He, H., & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. Hu, G., Hu, Y., Yang, K., Yu, Z., Sung, F., Zhang, Z., Xie, F., Liu, J., Robertson, N., Hospedales, T., & Miemie, Q. (2018). Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2706–2710. Hung, C.-C., Chen, Y.-J., Guo, S. J., & Hsu, F.-C. (2020). Predicting the price movement from candlestick charts: A CNN-based approach. International Journal of Ad Hoc and Ubiquitous Computing, 34(2), 111–120. Iwana, B. K., & Uchida, S. (2021). An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks. PLOS ONE, 16(7), e0254841. Jearanaitanakij, K., & Passaya, B. (2019). Predicting Short Trend of Stocks by Using Convolutional Neural Network and Candlestick Patterns. 2019 4th International Conference on Information Technology (InCIT), 159–162. Kobayashi, S. (2018). Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations (arXiv:1805.06201). arXiv. http://arxiv.org/abs/1805.06201 Komori, Y. (2020). Convolutional Neural Network for Stock Price Prediction Using Transfer Learning (SSRN Scholarly Paper No. 3756702). https://papers.ssrn.com/abstract=3756702 Kong, A., Zhu, H., & Azencott, R. (2021). Predicting intraday jumps in stock prices using liquidity measures and technical indicators. Journal of Forecasting, 40(3), 416–438. Kotsiantis, S., Kanellopoulos, D., & Pintelas, P. (2005). Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering, 30, 25–36. Kusuma, R. M. I., Ho, T.-T., Kao, W.-C., Ou, Y.-Y., & Hua, K.-L. (2019). Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market (arXiv:1903.12258). arXiv. http://arxiv.org/abs/1903.12258 Lu, T.-H., Shiu, Y.-M., & Liu, T.-C. (2012). Profitable candlestick trading strategies—The evidence from a new perspective. Review of Financial Economics, 21(2), 63–68. Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019). SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. Interspeech 2019, 2613–2617. http://arxiv.org/abs/1904.08779 Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend prediction. Expert Systems with Applications, 135, 60–70. Prado, H. A. do, Ferneda, E., Morais, L. C. R., Luiz, A. J. B., & Matsura, E. (2013). On the Effectiveness of Candlestick Chart Analysis for the Brazilian Stock Market. Procedia Computer Science, 22, 1136–1145. Schlüter, J., & Grill, T. (2015). Exploring Data Augmentation for Improved Singing Voice Detection with Neural Networks. https://www.semanticscholar.org/paper/Exploring-Data-Augmentation-for-Improved-Singing-Schl%C3%BCter-Grill/de277b6f639192b9eae84ab30838281ed12c0bbc Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1643–1647. Sezer, O. B., & Ozbayoglu, A. M. (2018). Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing, 70, 525–538. Sezer, O. B., Ozbayoglu, A. M., & Dogdu, E. (2017). An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework. Proceedings of the SouthEast Conference, 223–226. https://doi.org/10.1145/3077286.3077294 Sharma, N., Jain, V., & Mishra, A. (2018). An Analysis Of Convolutional Neural Networks For Image Classification. Procedia Computer Science, 132, 377–384. Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 60. Sim, H., Kim, H., & Ahn, J. (2019). Is Deep Learning for Image Recognition Applicable to Stock Market Prediction? Complexity, 2019, 1–10. Sinha, S., Mishra, S., Mishra, V., & Ahmed, T. (2022). Sector influence aware stock trend prediction using 3D convolutional neural network. Journal of King Saud University - Computer and Information Sciences, 34(4), 1511–1522. Taylor, L., & Nitschke, G. (2018). Improving Deep Learning with Generic Data Augmentation. 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 1542–1547. Teng, X., Wang, T., Zhang, X., Lan, L., & Luo, Z. (2020). Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method. Complexity, 2020, e6737951. Um, T. T., Pfister, F. M. J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., & Kulić, D. (2017). Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks. Proceedings of the 19th ACM International Conference on Multimodal Interaction, 216–220. https://doi.org/10.1145/3136755.3136817 Wang, H., Xu, J., Yan, R., Sun, C., & Chen, X. (2020). Intelligent Bearing Fault Diagnosis Using Multi-Head Attention-Based CNN. Procedia Manufacturing, 49, 112–118. Wang, Z., & Oates, T. (2015, January 1). Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks. Wu, H., Wang, Y., Lin, J., Yang, W., Wang, Y., & Zheng, Y. (2020). A Multi-Channel Multi-Head CNN Framework for Fault Classification in Industrial Process. 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), 366–371. Zheng, D., Li, H., & Zhu, X. (2015). Herding behavior in institutional investors: Evidence from China’s stock market. Journal of Multinational Financial Management, 32–33, 59–76.
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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