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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道 | zh_TW |
| dc.contributor.advisor | Yuh-Dauh Lyuu | en |
| dc.contributor.author | 徐賢翰 | zh_TW |
| dc.contributor.author | Hsien-Han Hsu | en |
| dc.date.accessioned | 2023-08-08T16:22:14Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-08 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-16 | - |
| dc.identifier.citation | A. A. Ariyo, A. O. Adewumi and C. K. Ayo, “Stock price prediction using the ARIMA model,” in 16th International Conference on Computer Modelling and Simulation, Cambridge, UK, Mar. 26–28, 2014.
B. Babcock, M. Datar, and R. Motwani, “Sampling from a moving window over streaming data,” in 2002 Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, 2002, pp. 633–634. R. Bardenet and B. Kégl, “Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm,” in 27th International Conference on Machine Learning, Haifa, Israel, 2010, pp. 55–62. Y. Bengio, P. Frasconi and P. Simard, “The problem of learning long-term dependencies in recurrent networks,” in IEEE International Conference on Neural Networks, San Francisco, 1993, pp. 1183–1188. Binance, “Binance-public-data.” https://github.com/binance/binance-public-data (accessed June 2, 2023). T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometerics, vol. 31, pp. 307–327, 1986. J. Chen, J. Du, Z. Xue and F. Kou, “Prediction of financial big data stock trends based on attention mechanism,” in 2020 IEEE International Conference on Knowledge Graph, Nanjing, China, 2020, pp. 152–156. CoinMarketCap, “Bitcoin.” https://coinmarketcap.com/currencies/bitcoin/ (accessed June 2, 2023). E. F. Fama, “Efficient capital markets: A review of theory and empirical work,” Journal of Finance, vol. 25, no. 2, pp. 383–417, 1970. M. J. Hamayel, A. Y. Owda, “A novel cryptocurrency price prediction model using GRU, LSTM and Bi-LSTM machine learning algorithm,” AI, vol. 2, no. 4, pp. 477–496, 2021. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, 1997, pp. 1735–1780. Y. T. Hu and F. Y. Xiao, “Network self attention for forecasting time series,” Applied Soft Computing, vol. 124, 2022, Art no. 109092. W. Huang, Y. Nakamori and S. Y. Wang, “Forecasting stock market movement direction with support vector machine,” Computers & Operations Research, vol. 32, pp. 2513–2522, 2005. R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and practice. Melbourne: OTexts, 2018. A. Jain, J. Mao and K. Mohiuddin, “Articial neural networks: A tutorial,” Computer, vol. 29, no. 3, 1996, pp. 31–44. W. W. Jiang, “Applications of deep learning in stock market prediction: Recent progress,” Expert Systems with Applications, vol. 184, 2021, Art no. 115537. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv:1412.6980, 2014. B. G. Malkiel, “The efficient market hypothesis and its critics,” Journal of Economic Perspectives, vol. 17, no. 1, pp. 59–82, 2003. S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” [Online]. Available: https://bitcoin.org/bitcoin.pdf. S. T. A. Niaki and S. Hoseinzade, “Forecasting S&P 500 index using artificial neural networks and design of experiments,” Journal of Industrial Engineering International, vol. 9, no. 1, 2013. A. Paszke et al., “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32, Vancouver, Canada, 2019. L. Prechelt, “Early stopping-but when?,” in Neural Networks: Tricks of the Trade, G. Montavon, G. B. Orr and KR. Müller, Eds., Berlin: Springer, 2022, pp. 55–69. Y. Qin, D. Song, H. Chen, W. Cheng, G. Jiang and G. Cottrell, “A dual-stage attention-based recurrent neural network for time series prediction,” arXiv:1704.02971, 2017. E. Ramos-Pérez, P. J. Alonso-González and J. J. Núñez-Velázquez, “Multi-transformer: A new neural network-based architecture for forecasting S&P volatility,” Mathematics, vol. 9, no.1794, 2021. Scikit-learn, “sklearn.preprocessing.” https://scikitlearn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html (accessed June 2, 2023). S. Siami-Namini, N. Tavakoli and A. S. Namin, “The performance of LSTM and BiLSTM in forecasting time series," in IEEE International Conference on Big Data, Los Angeles, 2019, pp. 3285–3292. S. Sridhar and S. Sanagavarapu, “Multi-head self-attention transformer for dogecoin price prediction,” in 14th International Conference on Human System Interaction, Gdańsk, Poland, 2021, pp. 1–6. M. A. I. Sunny, M. M. S. Maswood and A. G. Alharbi, “Deep learning-based stock price prediction using LSTM and bi-directional LSTM model,” in 2nd Novel Intelligent and Leading Emerging Sciences Conference, Giza, Egypt, 2020, pp. 87–92. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, 2017, pp. 5998–6008. C. H. Wu, C. C. Lu, Y. F. Ma and R. S. Lu, “A new forecasting framework for botcoin price with LSTM,”in IEEE International Conference on Data Mining Workshops, Singapore, Nov. 17–20, 2018, pp. 168–175. Y. Xu and S. B. Cohen, “Stock movement prediction from tweets and historical prices,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 2018, pp. 1970–1979. Y. Yao, “Data analysis on the computer intelligent stock prediction model based on LSTM RNN and algorithm optimization,” in IEEE International Conference on Electrical Engineering, Big Data and Algorithms, Changchun, China, Feb. 25–27, 2022, pp. 480–485. W. Yin, K. Kann, M. Yu and H. Schutze, “Comparetive study of CNN and RNN for natural language processing,” arXiv:1702.01923, 2017. Y. Yu, X. Si, C. Hu and J. Zhang, “A review of recurrent neural networks: LSTM cells and network architectures,” Neural Computation, vol. 31, 2019, pp. 1235–1270. Q. Zhang, Q. Chao, Y. Zhang, F. Bao, C. Zhang and P. Liu, “Transformer-based attention network for stock movement prediction,” Expert Systems with Application, vol. 202, 2022, Art no. 117239. Z. Zhang, H. N. Dai, J. Zhou, S. K. Mondal, M. M. García and H. Wang, “Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels,” Expert Systems with Applications, vol. 183, 2021, Art no. 115378. Z. Zhao, C. Xia, L. Chi, X. Chang, W. Li, T Yang and A. Y. Zomaya, “Short-term load forcasting based on the transformer model,” Information, vol. 12, no. 516, 2021. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88116 | - |
| dc.description.abstract | 隨著區塊鏈技術的發展，加密貨幣市場在近年來迅速普及與成長。比特幣作為這個領域最具代表性的加密貨幣，其價格的高度波動性不僅吸引了大量投資者和學術研究者的關注，也使得即時掌握短期價格走勢更為重要。然而，大多數現有的研究往往聚焦於使用日交易數據進行預測，而這對於快速波動的市場可能無法及時提供有價值的信息。
本論文著重於使用自注意力機制預測比特幣的短期價格走勢。通過自注意力機制，模型能夠捕捉時間序列中的長期依賴關係，結合更短的時間週期，使用更精細的K線和交易行為數據，使我們能夠捕捉到比特幣價格的細微變化。此外，通過位置編碼、滾動窗口驗證和早停法等機器學習技巧來強化模型的學習能力。結果表明，本模型在預測比特幣短期價格走勢上表現出色，達到了55.29%的準確率和0.561的F1分數，表現出比迴歸模型更優異的性能，也優於基於訓練資料漲跌比例作為預測機率的隨機選擇模型。兩者都證明了模型架構的有效性。 | zh_TW |
| dc.description.abstract | With the advancement of blockchain technology, the cryptocurrency market has experienced rapid proliferation and growth in recent years. Bitcoin, as the most emblematic cryptocurrency in this domain, possesses highly volatile prices, not only captivates a large number of investors and academic scholars but also accentuates the necessity for real-time monitoring of short-term price trends. Nevertheless, the majority of the existing research primarily concentrates on utilizing daily trading data for predictions, which may not adequately furnish timely insights in such a fluctuating market.
This study employs the self-attention mechanism to forecast the short-term price trend of Bitcoin. Through the self-attention mechanism, the model is adept at capturing long-term dependencies in time series data. By integrating this with shorter time frames and employing more granular candlestick and trading behavior data, we are able to discern the slight fluctuations in Bitcoin prices. Additionally, the study incorporates machine learning techniques such as positional encoding, rolling window validation, and early stopping to augment the model’s learning capabilities. The results illustrate that this model excels in predicting short-term price trends of Bitcoin, achieving an accuracy of 55.29% and an F1 score of 0.561. This exhibits superior performance in comparison with regression models. It also improves upon random selection models, which make predictions based on the proportions of upward and downward movements in the training data as prediction probabilities. Both corroborate the efficacy of the model architecture. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-08T16:22:14Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-08T16:22:14Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
摘要 II Abstract III 目錄 IV 圖目錄 VI 表目錄 VII 第一章 緒論 1 1.1 簡介 1 1.2 論文架構 2 第二章 背景 3 2.1 文獻回顧 3 2.2 類神經網路 4 2.2.1 前饋神經網路 4 2.2.2 遞迴神經網路 5 2.2.3 長短期記憶模型 6 2.3 自注意力模型相關文獻 8 2.3.1 自注意力模型 8 2.3.2 多頭自注意力模型 9 第三章 實驗方法 11 3.1 資料來源及處理 11 3.2 實驗設計 12 3.3 訓練與驗證資料 13 3.3.1 滾動窗口驗證 13 3.3.2 正規化 14 3.4 位置編碼 15 3.5 模型架構 16 第四章 實驗結果 18 4.1 評估指標 18 4.2 實驗結果 19 4.3 最佳模型 22 第五章 結論與展望 24 5.1 結論 24 5.2 未來展望 24 參考文獻 26 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 長短期記憶模型 | zh_TW |
| dc.subject | 時間序列 | zh_TW |
| dc.subject | 比特幣 | zh_TW |
| dc.subject | 自注意力機制 | zh_TW |
| dc.subject | 短期價格走勢預測 | zh_TW |
| dc.subject | Bitcoin | en |
| dc.subject | Self-Attention Mechanism | en |
| dc.subject | Short-Term Price Trend Prediction | en |
| dc.subject | Long Short-Term Memory | en |
| dc.subject | Time-Series Analysis | en |
| dc.title | 自注意力模型於比特幣短期價格走勢預測之應用 | zh_TW |
| dc.title | Application of Self-Attention Models for Short-Term Bitcoin Price Trend Forecasting | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 王釧茹;陸裕豪;金國興 | zh_TW |
| dc.contributor.oralexamcommittee | Chuan-Ju Wang;U-Hou Lok;Gow-Hsing King | en |
| dc.subject.keyword | 自注意力機制,比特幣,短期價格走勢預測,時間序列,長短期記憶模型, | zh_TW |
| dc.subject.keyword | Self-Attention Mechanism,Bitcoin,Short-Term Price Trend Prediction,Time-Series Analysis,Long Short-Term Memory, | en |
| dc.relation.page | 30 | - |
| dc.identifier.doi | 10.6342/NTU202301633 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-07-17 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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