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題名 利用模糊邏輯加強LSTM的預測準確性
Enhancing the predictive accuracy of LSTM using fuzzy logic
作者 吳宗翰
Wu, Tsung-Han
貢獻者 曾正男
吳宗翰
Wu, Tsung-Han
關鍵詞 LSTM預測
模糊邏輯
高斯模糊系統修正
三角模糊系統修正
股價預測
Long Short-Term Memory
Fuzzy Logic System
Gaussian membership functions
Triangular membership functions
Stock price prediction
日期 2025
上傳時間 4-Aug-2025 13:10:16 (UTC+8)
摘要 本研究主旨在透過結合長短期記憶（Long Short-Term Memory, LSTM）模型與模糊邏輯系統(Fuzzy Logic System)，提升股價預測之準確性與穩定性。以三種不同價位的股票台灣鴻海精密工業股份有限公司（股票代號：2317.TW）；台灣積體電路製造股份有限公司（股票代號：2330.TW）；陽明海運股份有限公司（股票代號：2609.TW）之歷史股價作為研究範例，建立雙層 LSTM 模型，並於預測誤差上導入高斯隸屬函數(Gaussian Membership Function)與三角形隸屬函數(Triangular Membership Function)所構成之模糊邏輯系統進行誤差修正。實驗結果顯示，融合模糊邏輯的 LSTM 模型在多次隨機預測中，其平均均方根誤差（Root Mean Square Error, RMSE）明顯低於單純使用 LSTM 的模型。該混合模型展現出更佳的預測穩定性，並提供更具彈性與可解釋性的預測結果。本研究顯示深度學習與模糊邏輯結合的潛力，未來可進一步應用於多種金融預測場景，以提升預測的準確度與模型透明度。
This study aims to enhance the accuracy and stability of stock price prediction by integrating the Long Short-Term Memory (LSTM) model with a Fuzzy Logic System (FLS). Historical stock price data from three Taiwanese companies with varying price levels—Hon Hai Precision Industry Co., Ltd. (2317.TW), Taiwan Semiconductor Manufacturing Company Limited (2330.TW), and Yang Ming Marine Transport Corporation (2609.TW)—are used as case studies. A two-layer LSTM model is constructed, and a fuzzy logic correction mechanism based on Gaussian and triangular membership functions is applied to adjust the prediction errors. Experimental results show that the LSTM model integrated with fuzzy logic consistently achieves lower average Root Mean Square Error (RMSE) across multiple randomized prediction trials compared to the standalone LSTM model. The proposed hybrid model demonstrates superior forecasting stability and provides more flexible and interpretable results. This study highlights the potential of combining deep learning with fuzzy logic systems and suggests future applications in various financial forecasting scenarios to improve predictive accuracy and model transparency.
參考文獻 • Chen, S.-M., & Lee, L.-C. (2010). Fuzzy time-series models for forecasting enrolments. Applied Soft Computing, 10(4), 1497–1504. • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder–decoder for statistical machine translation. Proceedings of EMNLP 2014, 1724–1734. • Fischer, T., & Krauss, C. (2018). Deep learning with long short term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. • Gers, F. A., & Schmidhuber, J. (2000). Recurrent nets that time and count. Proceedings of the IJCNN, 189–194. • Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2017). LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232. • Gulmez, A., Erdem, H., & Yilmaz, E. (2023). GA-attention-fuzzy-stock-net: Hybrid deep fuzzy system for intraday trading. Neurocomputing, 515, 310–324. • Hochreiter, S., & Schmidhuber, J. (1997). Long short‑term memory. Neural Computation, 9(8), 1735–1780. • Huang, P., Chen, T., & Kuo, Y. (2024). Gaussian versus triangular fuzzy post-compensation on deep neural stock predictors. Expert Systems with Applications (in press). • Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications. Prentice Hall. • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man‑Machine Studies, 7(1), 1–13. • Merity, S., Keskar, N. S., & Socher, R. (2018). Regularizing and optimizing LSTM language models. ICLR. • Porru, S., Pinna, A., & Marchesi, M. (2018). A fuzzy-rule-based portfolio selection system with dynamic risk adjustment. Expert Systems with Applications, 104, 113–123. • Ragab, A., Abdel-Aziz, M., & Ibrahim, H. (2024). Fuzzy BiLSTM with improved CPSO for cryptocurrency volatility forecasting. Applied Soft Computing, 146, 111226. • Ross, T. J. (2010). Fuzzy logic with engineering applications (3rd ed.). Wiley. • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in NIPS, 27, 3104–3112. • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116–132. • Wang, W., Liu, F., & Zhang, J. (2021). Fuzzy inference-based LSTM for long-term wind speed forecasting. Renewable Energy, 179, 1184–1198. • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. • Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249.
描述 碩士
國立政治大學
應用數學系
107751016
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107751016
資料類型 thesis
dc.contributor.advisor 曾正男zh_TW
dc.contributor.author (Authors) 吳宗翰zh_TW
dc.contributor.author (Authors) Wu, Tsung-Hanen_US
dc.creator (作者) 吳宗翰zh_TW
dc.creator (作者) Wu, Tsung-Hanen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 13:10:16 (UTC+8)-
dc.date.available 4-Aug-2025 13:10:16 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 13:10:16 (UTC+8)-
dc.identifier (Other Identifiers) G0107751016en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158367-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學系zh_TW
dc.description (描述) 107751016zh_TW
dc.description.abstract (摘要) 本研究主旨在透過結合長短期記憶（Long Short-Term Memory, LSTM）模型與模糊邏輯系統(Fuzzy Logic System)，提升股價預測之準確性與穩定性。以三種不同價位的股票台灣鴻海精密工業股份有限公司（股票代號：2317.TW）；台灣積體電路製造股份有限公司（股票代號：2330.TW）；陽明海運股份有限公司（股票代號：2609.TW）之歷史股價作為研究範例，建立雙層 LSTM 模型，並於預測誤差上導入高斯隸屬函數(Gaussian Membership Function)與三角形隸屬函數(Triangular Membership Function)所構成之模糊邏輯系統進行誤差修正。實驗結果顯示，融合模糊邏輯的 LSTM 模型在多次隨機預測中，其平均均方根誤差（Root Mean Square Error, RMSE）明顯低於單純使用 LSTM 的模型。該混合模型展現出更佳的預測穩定性，並提供更具彈性與可解釋性的預測結果。本研究顯示深度學習與模糊邏輯結合的潛力，未來可進一步應用於多種金融預測場景，以提升預測的準確度與模型透明度。zh_TW
dc.description.abstract (摘要) This study aims to enhance the accuracy and stability of stock price prediction by integrating the Long Short-Term Memory (LSTM) model with a Fuzzy Logic System (FLS). Historical stock price data from three Taiwanese companies with varying price levels—Hon Hai Precision Industry Co., Ltd. (2317.TW), Taiwan Semiconductor Manufacturing Company Limited (2330.TW), and Yang Ming Marine Transport Corporation (2609.TW)—are used as case studies. A two-layer LSTM model is constructed, and a fuzzy logic correction mechanism based on Gaussian and triangular membership functions is applied to adjust the prediction errors. Experimental results show that the LSTM model integrated with fuzzy logic consistently achieves lower average Root Mean Square Error (RMSE) across multiple randomized prediction trials compared to the standalone LSTM model. The proposed hybrid model demonstrates superior forecasting stability and provides more flexible and interpretable results. This study highlights the potential of combining deep learning with fuzzy logic systems and suggests future applications in various financial forecasting scenarios to improve predictive accuracy and model transparency.en_US
dc.description.tableofcontents 致謝...p1 摘要...p2 Abstract...p3 目錄...p4 第一章 緒論...p5 第一節 研究背景與動機...p5 第二節 研究問題與目標...p6 第三節 研究範圍與限制...p7 第四節 研究方法概述...p7 第五節 章節架構說明...p8 第二章 文獻回顧...p9 第一節 LSTM（Long Short-Term Memory）模型介紹...p9 第二節 模糊邏輯（Fuzzy Logic）系統介紹...p11 第三節 前人研究成果...p17 第四節 研究空間...p19 第三章 研究方法...p21 第一節 研究設計...p21 第二節 研究對象與取樣方法...p21 第三節 標準化數據...p23 第四節 LSTM模型建構...p24 第五節 模糊邏輯系統設置...p25 第六節 模糊預測與誤差補償流程...p29 第七節 預測表現評估指標...p32 第四章 研究結果...p33 第一節 預測結果呈現...p33 第二節 結果分析與比較...p36 第三節 統計顯著性檢定...p43 第五章 討論與建議...p45 第一節 研究發現之意涵...p45 第二節 理論與實務貢獻...p46 第三節 研究限制與未來發展...p47 第四節 實務建議...p48 參考文獻...p49 程式碼與參數設定...p51zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107751016en_US
dc.subject (關鍵詞) LSTM預測zh_TW
dc.subject (關鍵詞) 模糊邏輯zh_TW
dc.subject (關鍵詞) 高斯模糊系統修正zh_TW
dc.subject (關鍵詞) 三角模糊系統修正zh_TW
dc.subject (關鍵詞) 股價預測zh_TW
dc.subject (關鍵詞) Long Short-Term Memoryen_US
dc.subject (關鍵詞) Fuzzy Logic Systemen_US
dc.subject (關鍵詞) Gaussian membership functionsen_US
dc.subject (關鍵詞) Triangular membership functionsen_US
dc.subject (關鍵詞) Stock price predictionen_US
dc.title (題名) 利用模糊邏輯加強LSTM的預測準確性zh_TW
dc.title (題名) Enhancing the predictive accuracy of LSTM using fuzzy logicen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) • Chen, S.-M., & Lee, L.-C. (2010). Fuzzy time-series models for forecasting enrolments. Applied Soft Computing, 10(4), 1497–1504. • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder–decoder for statistical machine translation. Proceedings of EMNLP 2014, 1724–1734. • Fischer, T., & Krauss, C. (2018). Deep learning with long short term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. • Gers, F. A., & Schmidhuber, J. (2000). Recurrent nets that time and count. Proceedings of the IJCNN, 189–194. • Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2017). LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232. • Gulmez, A., Erdem, H., & Yilmaz, E. (2023). GA-attention-fuzzy-stock-net: Hybrid deep fuzzy system for intraday trading. Neurocomputing, 515, 310–324. • Hochreiter, S., & Schmidhuber, J. (1997). Long short‑term memory. Neural Computation, 9(8), 1735–1780. • Huang, P., Chen, T., & Kuo, Y. (2024). Gaussian versus triangular fuzzy post-compensation on deep neural stock predictors. Expert Systems with Applications (in press). • Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications. Prentice Hall. • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man‑Machine Studies, 7(1), 1–13. • Merity, S., Keskar, N. S., & Socher, R. (2018). Regularizing and optimizing LSTM language models. ICLR. • Porru, S., Pinna, A., & Marchesi, M. (2018). A fuzzy-rule-based portfolio selection system with dynamic risk adjustment. Expert Systems with Applications, 104, 113–123. • Ragab, A., Abdel-Aziz, M., & Ibrahim, H. (2024). Fuzzy BiLSTM with improved CPSO for cryptocurrency volatility forecasting. Applied Soft Computing, 146, 111226. • Ross, T. J. (2010). Fuzzy logic with engineering applications (3rd ed.). Wiley. • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in NIPS, 27, 3104–3112. • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116–132. • Wang, W., Liu, F., & Zhang, J. (2021). Fuzzy inference-based LSTM for long-term wind speed forecasting. Renewable Energy, 179, 1184–1198. • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. • Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249.zh_TW