請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55461
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 呂育道(Yuh-Dauh Lyuu) | |
dc.contributor.author | Yu-Lin Zhou | en |
dc.contributor.author | 周宇霖 | zh_TW |
dc.date.accessioned | 2021-06-16T04:03:45Z | - |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-03 | |
dc.identifier.citation | [1] S. Bai, J. Z. Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” 2018. [Online]. Available: arXiv:1803.01271. [2] Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE Transactions on Neural Networks and Learning Systems, vol. 5, no. 2, pp. 157–166, Mar. 1994, doi: 10.1109/72.279181. [3] A. E. Biondo, A. Pluchino, A. Rapisarda, and D. Helbing, “Are random trading strategies more successful than technical ones?” PLoS ONE, vol. 8, p. e68344, Jul. 2013, doi: 10.1371/journal.pone.0068344. [4] K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-Decoder approaches,” 2014. [Online]. Available: arXiv:1409.1259. [5] G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals, and Systems, vol. 2, no. 4, pp. 303–314, Dec. 1989, doi: 10.1007/BF02551274. [6] Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier, “Language modeling with gated convolutional networks,” 2017. [Online]. Available: arXiv:1612.08083. [7] P. Doetsch, M. Kozielski, and H. Ney, “Fast and robust training of recurrent neural networks for offline handwriting recognition,” In 14th International Conference on Frontiers in Handwriting Recognition, Crete, Greece, 2014, pp. 279–284, doi: 10.1109/ICFHR.2014.54. [8] J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, no. 2, pp. 179–211, Mar. 1990, doi: 10.1207/s15516709cog1402_1. [9] E. F. Fama, “Efficient capital markets: A review of theory and empirical work,” Journal of Finance, vol. 25, no. 2, pp. 383–417, May 1970, doi: 10.2307/2325486. [10] T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European Journal of Operational Research, vol. 270, no. 2, pp. 654–669, Oct. 2018, doi: 10.1016/j.ejor.2017.11.054. [11] J. Gehring, M. Auli, D. Grangier, and Y. N. Dauphin, “A convolutional encoder model for neural machine translation,” 2017. [Online]. Available: arXiv:1611.02344. [12] J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin, “Convolutional sequence to sequence learning,” 2017. [Online]. Available: axXiv:1705.03122. [13] A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A novel connectionist system for unconstrained handwriting recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp. 855–868, May 2009, doi: 10.1109/TPAMI.2008.137. [14] K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A search space odyssey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2222–2232, Oct. 2017, doi: 10.1109/TNNLS.2016.2582924. [15] R. Hamaguchi, A. Fujita, K. Nemoto, T. Imaizumi, and S. Hikosaka, “Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery,” In 2018 IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, pp. 1442–1450. [16] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 770–778, doi: 10.1109/CVPR.2016.90. [17] S. Hochreiter and J. Schmidhuber, “Long short-term memory”, Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735. [18] M. Holschneider, R. Kronland-Martinet, J. Morlet, and P. Tchamitchian, “A real-time algorithm for signal analysis with the help of the wavelet transform,” Wavelets, J. M. Combes, A. Grossmann, and Ph. Tchamitchian, Eds, Heidelberg: Springer, 1990, pp. 286–297. [19] R. Jozefowicz, W. Zaremba, and I. Sutskever, “An empirical exploration of recurrent network architectures,” In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015, pp. 2342–2350. [20] N. Kalchbrenner, L. Espeholt, K. Simonyan, A. van den Oord, A. Graves, and K. Kavukcuoglu, “Neural machine translation in linear time,” 2017. [Online]. Available: arXiv:1610.10099. [21] M. Hiransha, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “NSE stock market prediction using deep-learning models,” Procedia Computer Science, vol. 132, pp. 1351–1362, 2018, doi: 10.1016/j.procs.2018.05.050. [22] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” In Proceedings of Advances in Neural Information Processing Systems, Lake Tahoe, NV, 2012, pp. 1106–1114. [23] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998, doi: 10.1109/5.726791. [24] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” In IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015, pp. 3431–3440, doi: 10.1109/CVPR.2015.7298965. [25] B. G. Malkiel, A Random Walk Down Wall Street, 9th ed. New York: Norton, 1973. [26] G. Melis, C. Dyer, and P. Blunsom, “On the state of the art of evaluation in neural language models,” 2017. [Online]. Available: arXiv:1707.05589. [27] D. M. Q. Nelson, A. C. M. Pereira, and R. A. de Oliveira, “Stock market’s price movement prediction with LSTM neural networks,” In 2017 International Joint Conference on Neural Networks, Anchorage, pp. 1419–1426, doi: 10.1109/IJCNN.2017.7966019. [28] A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “WaveNet: A generative model for raw audio,” 2016. [Online]. Available: arXiv:1609.03499. [29] V. Pham, T. Bluche, C. Kermorvant, and J. Louradour, “Dropout improves recurrent neural networks for handwriting recognition” In 14th International Conference on Frontiers in Handwriting Recognition, Crete, Greece, 2014, pp. 285–290, doi: 10.1109/ICFHR.2014.55. [30] R. Raina, A. Madhavan, and A. Y. Ng, “Large-scale deep unsupervised learning using graphics processors,” In Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, 2009, pp. 873–880, doi: 10.1145/1553374.1553486. [31] T. Salimans and D. P. Kingma, “Weight normalization: A simple reparameterization to accelerate training of deep neural networks,” In Proceedings of Advances in Neural Information Processing Systems, Barcelona, 2016, pp. 901–909. [32] S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “Stock price prediction using LSTM, RNN and CNN-sliding window model,” In 2017 International Conference on Advances in Computing, Communications and Informatics, Udupi, India, pp. 1643–1647, doi: 10.1109/ICACCI.2017.8126078. [33] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdionv, “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, Jan. 2014. [34] I. Sutskever, J. Martens, and G. E. Hinton, “Generating text with recurrent neural networks,” In Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, 2011, pp. 1017–1024. [35] I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” In Proceedings of Advances in Neural Information Processing Systems, Montreal, 2014, pp. 3104–3112 [36] P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell, “Understanding convolution for semantic segmentation,” In 2018 IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, pp. 1451–1460, doi: 10.1109/WACV.2018.00163. [37] Z. Wang and S. Ji, “Smoothed dilated convolutions for improved dense prediction,” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, London, 2018, pp. 2486–2495, doi: 10.1145/3219819.3219944. [38] H. White, “Economic prediction using neural networks: the case of IBM daily stock returns,” In Proceedings of International Conference on Neural Networks, San Diego, 1988, pp. 451–458, doi: 10.1109/ICNN.1988.23959. [39] J. Yao, C. L. Tan, and H. L. Poh, “Neural networks for technical analysis: A study on KLCI,” International Journal of Theoretical and Applied Finance, vol. 2, no. 2, pp. 221–241, Jul. 1999, doi: 10.1142/S0219024999000145. [40] F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” In 4th International Conference on Learning Representation, San Juan, Puerto Rico, 2016, pp. 1–13. [41] W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent neural network regularization,” 2015. [Online]. Available: arXiv:1409.2329. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55461 | - |
dc.description.abstract | 股票市場是個變化劇烈且快速的金融市場,外加有大量雜訊的干擾,使得預測股票市場的漲跌是件困難的事情。類神經網路具有強大的函數模擬能力,因此適合利用股票市場所產生的大量非線性資料來學習並預測。相對於時間序列建模常使用的 LSTM,本論文使用時間卷積網路 (TCN) 此一基於 CNN 但被特別設計的適合處理時間序列的類神經網路,將之用於台灣股票市場漲跌的預測。 擴張卷積是 TCN 的關鍵架構,其指數成長的擴張因子讓 TCN 在相同的參數量下,可以讓感受野 (receptive field) 以指數方式成長,使其獲得足夠多的歷史資訊。然而多項研究指出這種指數成長的擴張因子會導致 gridding 的問題,因而損及類神經網路的預測能力。本論文基於混合式擴張卷積提出覆蓋式擴張卷積網路 (C-TCN) 改善此一問題,並與原本的 TCN 一同比較雙方在預測加權報酬指數與化學生技醫療報酬指數漲跌的能力。實驗結果顯示,在兩個標的各六個時間窗口共十二次實驗中,C-TCN 在其中十個實驗勝過或持平 TCN,有達到改善的效果。 | zh_TW |
dc.description.abstract | Stock market is a very complex financial system. Stock price can rise and fall dramatically in a short period of time. With a high degree of noise, prediction of stock market is a difficult task. Neural network is a powerful data-driven model that can approximate arbitrary continuous function. This makes it a good choice to learn from the enormous non-linear data generated by the stock market. Instead of using LSTM, which is known for being good at predicting time series, we use temporal convolutional network (TCN) on stock market prediction. TCN is an architecture based on convolutional neural network (CNN), and combines the best practice of recent convolutional architectures for sequence modeling. Dilated convolution is a key of TCN, as the exponentially increased dilation factors can expand the receptive field exponentially without requiring extra parameters. This allows TCN to have longer effective history information in an efficient way. However, many researches pointed out that the dilated convolution using exponentially increased dilation factors suffers from gridding effect, which hampers the performance of the neural network. On top of TCN, we propose C-TCN, which is based on hybrid dilated convolution, to address this problem and compared it with the original TCN on the prediction accuracy of TAIEX Total Return Index and Chemical, Biotechnology and Medical Care Total Return Index. We use 6 sliding windows for each target. The results show that C-TCN is better than or equal to TCN in 10 out of the 12 experiments. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T04:03:45Z (GMT). No. of bitstreams: 1 U0001-2907202022214800.pdf: 2079406 bytes, checksum: 831da138cdcf7676f553798dc9ce6d19 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 誌謝 ii 摘要 iii Abstract iv 目錄 v 圖目錄 vii 表目錄 viii 1 緒論 1 1.1 研究動機 1 1.2 論文架構 2 2 背景 4 2.1 類神經網路文獻回顧 4 2.2 類神經網路預測股價文獻回顧 6 2.3 模型簡介 6 2.3.1 全卷積網路 6 2.3.2 擴張卷積 6 2.3.3 殘差網路 10 2.3.4 時間卷積網路 10 3 方法設計 14 3.1 擴張卷積面臨的問題 14 3.2 提出的方法 16 4 實驗設計與結果 19 4.1 資料來源及處理 19 4.2 實驗設計 19 4.3 實驗結果 19 5 結論與未來展望 22 5.1 結論 22 5.2 未來展望 22 參考文獻 24 | |
dc.language.iso | zh-TW | |
dc.title | 基於混合式擴張卷積改善時間卷積網路預測類股趨勢的能力 | zh_TW |
dc.title | Improved Temporal Convolutional Network on Stock Trends Prediction Based on Hybrid Dilated Convolution | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張經略(Ching-Lueh Chang),金國興(Gow-Hsing King),陸裕豪(U-Hou Lok) | |
dc.subject.keyword | 卷積類神經網路,時間卷積網路,擴張卷積,股價預測, | zh_TW |
dc.subject.keyword | Convolutional neural network,Temporal convolutional network,Dilated convolution,Stock price prediction, | en |
dc.relation.page | 29 | |
dc.identifier.doi | 10.6342/NTU202002065 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-04 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-2907202022214800.pdf 目前未授權公開取用 | 2.03 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。