政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/130998

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/130998

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113313/144292 (79%)
造访人次 : 50947379 在线人数 : 948

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 商學院 > 金融學系 > 學位論文 > Item 140.119/130998

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/130998

题名:	基於古典與量子卷積神經網絡的時間序列圖像分析 Time Series Image Analysis by Classical and Quantum Convolutional Neural Networks
作者:	周琪 Zhou, Qi
贡献者:	廖四郎 Liao, Szu-Lang 周琪 Zhou, Qi
关键词:	時間序列圖像化古典卷積神經網路量子卷積神經網絡圖像分類趨勢預測 Imaging time series Classical convolutional neural network Quantum convolutional neural network Image classification Trend prediction
日期:	2020
上传时间:	2020-08-03 17:39:47 (UTC+8)
摘要:	時間序列是一維數據，但我們可以將其轉換成二維矩陣，最終圖像化後成三維張量。圖像化時間序列的映射需要能夠保留時間依賴性等時間序列重要特徵。卷積神經網絡是深度學習中一種重要的神經網絡，在計算機視覺領域有著非常多的應用，其對視覺信息的處理能力非常突出。所以我們將時間序列圖像和卷積神經網絡結合，研究從高維數據上提取數據特徵的可行性與能力，並最終用來進行時間序列圖像分類和未來趨勢預測。研究結果顯示，卷積神經網絡在時間序列圖像分類和未來趨勢預測上有良好表現，其策略表現可以超越基準線並獲得正的累積收益。另外，我們也對量子卷積神經網絡做了初步探討，我們闡述了相關理論，同時模擬構建了一個量子卷積神經網絡模型。研究結果顯示，量子卷積神經網絡是可行有效的，且在時間序列圖像分類和未來趨勢預測上能夠達到古典卷積神經網絡的水準。量子卷積神經網絡具有很大潛力且值得被研究。 Time series is a one dimensional data, but we can transform it to be a two dimensional matrix, and finally obtain a time series image, which is a three dimensional tensor. A map of imaging time series should preserve some important properties of the time series such as the temporal dependency. Convolutional neural network is a kind of neural networks in deep learning, it is widely applied in the field of computer vision for its strong visual information processing ability. Therefore, we combine the time series image and the convolutional neural network together to research the feasibility and the ability of extracting features from the high dimensional data, and use the model to do the time series image classification and the future trend prediction. The result shows that the convolutional neural network has a good performance on time series image classification and future trend prediction. It could beat the baseline and has a positive cumulative return. In addition, we do a preliminary research on the quantum convolutional neural network. We describe the relative theory and simulate a quantum convolutional neural network model. The result shows that the quantum convolutional neural network is feasible and could reach the similar level of the classical convolutional neural network on both time series image classification and future trend prediction. Quantum convolutional neural network is potential and deserves to be studied.
參考文獻:	[1] Cong, I., Choi, S., & Lukin, M. D. (2019). Quantum Convolutional Neural Networks. Nature Physics, 15, 1273-1278. [2] Eckman, J. P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence Plots of Dynamical Systems. Europhysics Letters, 4 (91), 973-977. [3] Erhan, D., Bengio, Y., Courville, A., Manzagol, P. A., & Vincent, P. (2010). Why Does Unsupervised Pre-training Help Deep Learning? Journal of Machine Learning Research, 11, 625-660. [4] Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep Learning for Finance: Deep Portfolios. Applied Stochastic Models in Business and Industry, 33, 3-12. [5] Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep Portfolio Theory. arXiv:1605.07230. [6] Kavukcuoglu, K., Sermanet, P., Boureau, Y. L., Gregor, K., Mathieu, M., & LeCun, Y. (2010). Learning Convolutional Feature Hierarchies for Visual Recognition. Neural Information Processing Systems, 1, 1090-1098. [7] Kaye, P., Laflamme, R., & Mosca, M. (2019). An Introduction to Quantum Computing. Oxford University Press. [8] Kerenidis, I., & Prakash, A. (2016). Quantum Recommendation Systems. Innovations in Theoretical Computer Science Conference, 49, 1-21. [9] Kerenidis, I., Landman, J., Luongo, A., & Prakash, A. (2018). Q-means: A Quantum Algorithm for Unsupervised Machine Learning. Neural Information Processing Systems. [10] Kerenidis, I., Landman, J., & Prakash, A. (2020). Quantum Algorithms for Deep Con- volutional Neural Network. International Conference on Learning Representations. [11] Kitaev, A. Y., Shen, A. H., & Vyalyi, M. N. (1999). Classical and Quantum Computa- tion. American Mathematical Society. [12] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet Classification with Deep Convolutional Neural Networks. Communications of the Association for Computing Machinery, 60(6), 84-90. [13] Lai, C. (2018). Analysis of the predictive ability of time series using convolutional neural network. National Cheng-Chi University. [14] Le, Q. V., Ngiam, J., Chen, Z., Chia, D., Koh, P. W., & Ng, A. Y. (2010). Tiled Convolutional Neural Networks. Neural Information Processing Systems, 1, 1279-1287. [15] LeCun, Y., & Bengio, Y. (1995). Convolutional Networks for Images, Speech, and Time Series. The Handbook of Brain Theory and Neural Networks, 255-258. [16] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based Learning Ap- plied to Document Recognition. Proceedings of the Institute of Electrical and Electronics Engineers, 86(11), 2278-2324. [17] Martin, T., Hagan, M. T., Demuth, H. B., Beale, M. H., & Jesús, O. D. (2014). Neural Network Design. Martin Hagan. [18] Nakahara, M., & Ohmi, T. (2008). Quantum Ccomputing From Linear Algebra to Physical Realizations. Chemical Rubber Company Press. [19] Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Infor- mation. University Press of Cambridge. [20] Sakurai, J. J., & Napolitano, J.J. (2014). Modern Quantum Mechanics. Pearson Edu- cation Limited. [21] Scherer, W. (2019). Mathematics of Quantum Computing. Springer Nature Switzerland AG. [22] Shankar, R. (1994). Principles of Quantum Mechanics. Plenum Press. [23] Susskind, L., & Friedman, A. (2014). Quantum Mechanics, The Theoretical Minimum. Perseus Books Group. [24] Wang, Z., & Oates, T. (2015). Imaging Time-Series to Improve Classification and Impu- tation. Proceedings of the International Conference on Artificial Intelligence, 3939-3945. [25] Wu, J. (2017). Introduction to Convolutional Neural Networks. Nanjing University. [26] Zettili, N. (2009). Quantum Mechanics Concepts and Applications. John Wiley & Sons Limited.
描述:	碩士國立政治大學金融學系 107352038
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0107352038
数据类型:	thesis
DOI:	10.6814/NCCU202001059
显示于类别:	[金融學系] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
203801.pdf		3002Kb	Adobe PDF2	106	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈