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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/102766完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道 | zh_TW |
| dc.contributor.advisor | Yuh-Dauh Lyuu | en |
| dc.contributor.author | 李展瑋 | zh_TW |
| dc.contributor.author | Jan-Wei Li | en |
| dc.date.accessioned | 2026-07-22T16:13:27Z | - |
| dc.date.available | 2026-07-23 | - |
| dc.date.copyright | 2026-07-22 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-07-17 | - |
| dc.identifier.citation | [1] M. Khairul Anam, Sarjon Defit, Haviluddin, Lusiana Efrizoni, and Muhammad Bambang Firdaus. Early stopping on CNN-LSTM development to improve classification performance. J. Appl. Data Sci., 5(3):1175–1188, 2024.
[2] Ran Aroussi. yfinance: Yahoo! Finance market data downloader. https://github.com/ranaroussi/yfinance, 2019. GitHub repository. [3] Christopher M.Bishop. PatternRecognitionandMachineLearning. Springer, 2006. [4] TimBollerslev. Generalizedautoregressive conditional heteroskedasticity. J. Econometrics, 31(3):307–327, 1986. [5] George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. Time Series Analysis: Forecasting and Control. John Wiley & Sons, 2015. [6] Leo Breiman. Random forests. Machine Learning, 45(1):5–32, 2001. [7] Jou-Fan Chen, Wei-Lun Chen, Chun-Ping Huang, Szu-Hao Huang, and An-Pin Chen. Financial time-series data analysis using deep convolutional neural networks. In Proc. 7th Int. Conf. Cloud Comput. Big Data (CCBD), pages 87–92. IEEE, 2016. [8] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine Learning, 20(3):273–297, 1995. [9] Jean-Pierre Eckmann, S. Oliffson Kamphorst, and David Ruelle. Recurrence plots of dynamical systems. Europhys. Lett., 4(9):973, 1987. [10] Robert F. Engle. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, pages 987–1007, 1982. [11] Eugene F. Fama. Efficient capital markets: a review of theory and empirical work. J. Finance, 25(2):383–417, 1970. [12] Rafael C. Gonzalez and Richard E. Woods. Digital Image Processing. Pearson Education, 4th edition, 2018. [13] Nima Hatami, Yann Gavet, and Johan Debayle. Classification of time-series images using deep convolutional neural networks. In Proc. 10th Int. Conf. Mach. Vision (ICMV), volume 10696, page UNSP 106960Y, 2017. [14] PeterJ.Huber. Robustestimationofalocationparameter. Ann.Math.Stat., 35(1):73-101, 1964. [15] Sergey Ioffe and Christian Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proc. Int. Conf. Mach. Learn. (ICML), pages 448–456, 2015. [16] Kenneth I. Laws. Rapid texture identification. In Proc. SPIE Image Processing for Missile Guidance, volume 238, pages 376–381, 1980. [17] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. [18] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pages 2980–2988, 2017. [19] Wanan Liu, Hong Fan, Min Xia, and Congyuan Pang. Predicting and interpreting financial distress using a weighted boosted tree-based tree. Eng. Appl. Artif. Intell., 116:105466, 2022. [20] Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. Rectifier nonlinearities improve neural network acoustic models. In Proc. 30th Int. Conf. Mach. Learn. (ICML), volume 28, page 3, 2013. [21] OmerBerat Sezer, Mehmet Ugur Gudelek, and A. Murat Ozbayoglu. Financial time series forecasting with deep learning: a systematic literature review: 2005-2019. Appl. Soft Comput., 90:106181, 2020. [22] OmerBeratSezer andA.MuratOzbayoglu. Algorithmic financial trading with deep convolutional neural networks: time series to image conversion approach. Appl. Soft Comput., 70:525–538, 2018. [23] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res., 15(1):1929–1958, 2014. [24] ZhiguangWangandTimOates. Encodingtimeseriesasimagesforvisualinspection and classification using tiled convolutional neural networks. In Proc. 29th AAAI Conf. Artif. Intell. (AAAI) Workshops, 2015. [25] Zhiguang Wang and Tim Oates. Imaging time-series to improve classification and imputation. arXiv preprint arXiv:1506.00327, 2015. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/102766 | - |
| dc.description.abstract | 本論文針對金融時間序列預測中模型訓練提出一整合框架。透過格拉姆角場（Gramian angular field, GAF）將股價序列影像化，並結合 Laws 紋理能量測量（Laws' texture energy measures, Laws' TEM）提取影像局部特徵。實驗結果顯示，在 6 至 16 天之不同視窗長度測試下，本論文之 Laws-CNN 模型於標普 500 指數與台灣加權指數等標的中，在中位數準確率與模型穩定性上優於基準模型。 | zh_TW |
| dc.description.abstract | This thesis proposes an integrated framework for model training in financial time series prediction. By transforming stock price sequences into images via Gramian angular field (GAF) and incorporating Laws' texture energy measures (Laws' TEM) to extract local image features, the proposed framework effectively enhances model robustness. Experimental results indicate that under window sizes ranging from 6 to 16 days, the proposed Laws-CNN model outperforms the baseline models in terms of median accuracy and model stability across representative benchmarks, including the S\&P 500 and the Taiwan Capitalization Weighted Stock Index (TWII). | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-07-22T16:13:27Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-07-22T16:13:27Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
誌謝 iii 摘要 v Abstract vii 目次 ix 圖次 xi 表次 xiii 第一章 緒論 1 1.1 簡介 1 1.2 論文架構 2 第二章 背景知識與相關研究 3 2.1 金融預測技術回顧 3 2.2 時間序列影像化技術：格拉姆角場(GAF) 4 2.3 卷積神經網路(CNN) 4 2.4 Laws紋理能量測量(Laws’TEM) 5 第三章 研究方法 7 3.1 資料來源與預處理 7 3.1.1 實驗標的選擇 7 3.1.2 資料正規化與標籤定義 7 3.2 影像特徵生成流程 8 3.2.1 格拉姆角場轉換 8 3.2.2 Laws濾波器卷積與能量地圖計算 10 3.2.3 Laws-CNN網路架構設計 14 3.2.4 損失函數與權重平衡 15 3.3 實驗設計與評估準則 15 3.3.1 試驗(trial)之定義與流程 16 3.3.2 實驗場次(session)與穩定性評估指標 16 3.3.3 資料分割比例與測試樣本規模 17 3.4 實驗環境與參數設定 18 第四章 實驗結果與分析 21 4.1 預測準確率分析：中位數表現探討 21 4.2 模型穩定性分析：標準差探討 23 4.3 綜合評估與討論 24 第五章 結論與未來展望 27 5.1 結論 27 5.2 未來展望 27 參考文獻 29 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 格拉姆角場 | - |
| dc.subject | Laws 紋理能量測量 | - |
| dc.subject | 卷積神經網路 | - |
| dc.subject | 股市趨勢預測 | - |
| dc.subject | 穩定性評估 | - |
| dc.subject | Gramian Angular Field (GAF) | - |
| dc.subject | Laws’ Texture Energy Measures (Laws’ TEM) | - |
| dc.subject | Convolutional Neural Networks (CNN) | - |
| dc.subject | Stock Trend Prediction, Stability Evaluation | - |
| dc.title | Laws 紋理增強 GAF-CNN 之股市趨勢預測 | zh_TW |
| dc.title | Laws-Enhanced GAF-CNN for Stock Trend Prediction | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 張經略;陸裕豪 | zh_TW |
| dc.contributor.oralexamcommittee | Ching-Lueh Chang;U Hou Lok | en |
| dc.subject.keyword | 格拉姆角場; Laws 紋理能量測量; 卷積神經網路; 股市趨勢預測; 穩定性評估 | zh_TW |
| dc.subject.keyword | Gramian Angular Field (GAF); Laws’ Texture Energy Measures (Laws’ TEM); Convolutional Neural Networks (CNN); Stock Trend Prediction, Stability Evaluation | en |
| dc.relation.page | 31 | - |
| dc.identifier.doi | 10.6342/NTU202602084 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2026-07-17 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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