混合式機器學習於多目標時間序列預測的研究

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陳立洋(Li-Yang Chen) 查詢紙本館藏

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資訊管理學系

論文名稱

混合式機器學習於多目標時間序列預測的研究
(A Study of Hybrid Machine Learning for Multi-target Time Series Prediction)

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至系統瀏覽論文 (2026-7-1以後開放)

摘要(中)

時間序列預測是目前非常重要的領域，在金融市場時間序列中，股票預測是較多人所關注的議題，不論是企業或個人投資客都期望能夠透過預測股價，來獲得經濟收益。本研究使用了非線性的模型預測方法來進行股票預測，使用的模型稱為球型複數類神經模糊推理系統(Sphere complex based neuro-fuzzy inference system, SCNFIS)，SCNFIS為一種複數的類神經模糊系統，可以分為前鑑部和後鑑部兩個部分，其透過球型複數模糊集(Sphere Complex Fuzzy Sets, SCFSs)來實現多目標的預測。相較於過往的研究僅預測單一目標，此方法更符合投資人分散風險、同時觀察多個目標以製定投資策略的需求。為了選出對於多個目標皆有貢獻的特徵，本研究使用了多目標特徵選取方法以選出最佳特徵來訓練模型。在SCNFIS中使用了複數減法聚類法(Subtractive clustering for complex-valued data, SCC)來建立模型的前鑑部，其將原本的減法聚類法的實數範圍擴展至複數範圍。為了解決非線性模型會出現的過度擬合和參數最佳化問題，本研究提出了一種混合式的機器學習方法稱為GDWOA-ACO_R-RLSE來最佳化SCNFIS的參數，其透過分治法(The Divide-and-conquer method)的概念，使用GDWOA-ACO_R用於最佳化前鑑部的參數，RLSE用於最佳化後鑑部的參數。最後，進行三個實驗來評估所提出的研究方法的可行性。實驗結果顯示和過去文獻相比具有更好的預測性能，驗證了該方法的有效性。

摘要(英)

Time series forecasting is currently a very important field. In the financial market, stock prediction is a topic of great interest. Both businesses and individual investors hope to achieve economic gains through stock price forecasting. This study uses a nonlinear modeling approach to stock prediction, employing a model known as the Sphere Complex based Neuro-Fuzzy Inference System (SCNFIS). SCNFIS is a type of complex-valued neuro-fuzzy system, consisting of a front-end and a back-end. It uses Sphere Complex Fuzzy Sets (SCFSs) to achieve multi-objective forecasting. Compared to previous studies that only predict a single target, this method better aligns with the needs of investors to diversify risks and simultaneously observe multiple targets to formulate investment strategies. To select features that contribute to multiple targets, this study employs a multi-objective feature selection method to choose the best features for training the model. In SCNFIS, a complex-valued subtractive clustering method (SCC) is used to establish the antecedent parts of the model, extending the range of the original subtractive clustering method from real numbers to complex numbers. To address issues of overfitting and parameter optimization in non-linear models, this study proposes a hybrid machine learning method called GDWOA-ACO_R-RLSE to optimize the parameters of SCNFIS. This method uses the concept of the Divide-and-Conquer method, employing GDWOA-ACO_R to optimize the parameters of the front-end and RLSE to optimize the parameters of the back-end. Finally, three experiments are conducted to evaluate the feasibility of the proposed research method. The experimental results show better forecasting performance compared to previous literature, confirming the effectiveness of the method.

關鍵字(中)

★ 時間序列預測
★ 混合式機器學習演算法
★ 多目標預測
★ 複數類神經模糊推理系統
★ 球型複數模糊集

關鍵字(英)

★ Time series prediction
★ Hybrid machine learning method
★ Multi-target prediction
★ Complex neural fuzzy inference system
★ Sphere complex fuzzy set

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
專有名詞及縮寫字說明表 viii
專有名詞及縮寫字說明表 (續) ix
記號使用說明表 x
記號使用說明表 (續) xi
第一章緒論 1
1.1 研究動機和目的 1
1.2 研究方法概述 2
1.3 論文架構 3

第二章文獻探討 4
2.1 特徵選取 4
2.2 複數模糊集 5
2.3 ANFIS模型 5
2.4 分群演算法 6
2.5 最佳化演算法 6
2.6 混合元啟發式演算法 7

第三章研究方法 8
3.1 多目標特徵選取 8
3.1.1 影響資訊矩陣 8
3.1.2 多目標特徵選取演算法 10
3.2 前鑑部的構成和選擇 12
3.2.1 前鑑部的構成 12
3.2.2 前鑑部的選擇 14
3.3 球型複數模糊集 15
3.4 SCNFIS模型 17
3.5 混合式機器學習演算法 20
3.5.1 基於菁英主義的混合方法 20
3.5.2 高斯分佈鯨群最佳化演算法 21
3.5.3 連續蟻群最佳化演算法 24
3.5.4 遞迴式最小平方估計法 26
3.5.5 GDWOA-ACO_R-RLSE 28

第四章實驗 31
4.1 實驗一：單目標預測 32
4.2 實驗二：雙目標預測 38
4.3 實驗三：多目標預測 43

第五章討論 48
5.1 研究方法的討論 48
5.2 實驗結果的討論 49
5.2.1 單目標預測實驗 (TAIEX) 49
5.2.2 雙目標預測實驗 (DJIA, TAIEX) 50
5.2.3 多目標預測實驗 (HSI, N225, SP500, SSEC) 50

第六章結論與未來的研究方向 52
6.1 結論與貢獻 52
6.2 未來的研究方向 52

參考文獻 54

參考文獻

[1] A. Sagheer and M. Kotb, “Time series forecasting of petroleum production using deep LSTM recurrent networks,” Neurocomputing, vol. 323, pp. 203–213, Jan. 2019

[2] G. Kumar, S. Jain, and U. P. Singh, “Neuro-fuzzy and particle swarm optimization based hybrid approach for stock price forecasting,” in 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), Mar. 2021, pp. 753–758.

[3] N. Zhang, A. Lin, and P. Shang, “Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 477, pp. 161–173, Jul. 2017

[4] P. C. S. Bezerra and P. H. M. Albuquerque, “Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels,” Computational Management Science, vol. 14, no. 2, pp. 179–196, Apr. 2017.

[5] W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, “A CNN-LSTM-based model to forecast stock prices,” Complexity, vol. 2020, pp. 1–10, Nov. 2020.

[6] L. Menkhoff, “Examining the use of technical currency analysis,” International Journal of Finance & Economics, vol. 2, no. 4, pp. 307–318, 1997.

[7] A. COSER, M. Maer Matei, and C. ALBU, “Predictive models for loan default risk assessment,” Economic Computation and Economic Cybernetics Studies and Research, vol. 53, pp. 149–165, Jun. 2019.

[8] C. Jung and R. Boyd, “Forecasting UK stock prices,” Applied Financial Economics, vol. 6, no. 3, pp. 279–286, Jun. 1996.

[9] W. Bessler and P. Lückoff, “Predicting stock returns with bayesian vector autoregressive models,” in Data Analysis, Machine Learning and Applications, C. Preisach, H. Burkhardt, L. Schmidt-Thieme, and R. Decker, Eds., in Studies in Classification, Data Analysis, and Knowledge Organization. Berlin, Heidelberg: Springer, 2008, pp. 499–506.

[10] A. A. Ariyo, A. O. Adewumi, and C. K. Ayo, “Stock price prediction using the ARIMA model,” in 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, Mar. 2014, pp. 106–112.

[11] B. M. A. Awartani and V. Corradi, “Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries,” International Journal of Forecasting, vol. 21, no. 1, pp. 167–183, Jan. 2005.

[12] M. Abd Elaziz, A. A. Ewees, and Z. Alameer, “Improving adaptive neuro-fuzzy inference system based on a modified salp swarm algorithm using genetic algorithm to forecast crude oil price,” Natural Resources Research, vol. 29, no. 4, pp. 2671–2686, Aug. 2020.

[13] A. Kazem, E. Sharifi, F. K. Hussain, M. Saberi, and O. K. Hussain, “Support vector regression with chaos-based firefly algorithm for stock market price forecasting,” Applied Soft Computing, vol. 13, no. 2, pp. 947–958, Feb. 2013.

[14] Y.-K. Kwon and B.-R. Moon, “A hybrid neurogenetic approach for stock forecasting,” IEEE Transactions on Neural Networks, vol. 18, no. 3, pp. 851–864, May 2007.

[15] M. Qi and G. P. Zhang, “Trend time–series modeling and forecasting with neural networks,” IEEE Transactions on Neural Networks, vol. 19, no. 5, pp. 808–816, May 2008

[16] L. Yu, S. Wang, and K. K. Lai, “A neural-network-based nonlinear metamodeling approach to financial time series forecasting,” Applied Soft Computing, vol. 9, no. 2, pp. 563–574, Mar. 2009.

[17] C.-F. Liu, C.-Y. Yeh, and S.-J. Lee, “Application of type-2 neuro-fuzzy modeling in stock price prediction,” Applied Soft Computing, vol. 12, no. 4, pp. 1348–1358, Apr. 2012.

[18] L. J. Cao and F. E. H. Tay, “Support vector machine with adaptive parameters in financial time series forecasting,” IEEE Transactions on Neural Networks, vol. 14, no. 6, pp. 1506–1518, Jan. 2003.

[19] J. L. Ticknor, “A bayesian regularized artificial neural network for stock market forecasting,” Expert Systems with Applications, vol. 40, no. 14, pp. 5501–5506, Oct. 2013.

[20] M. M. Gupta and D. H. Rao, “On the principles of fuzzy neural networks,” Fuzzy Sets and Systems, vol. 61, no. 1, pp. 1–18, Jan. 1994.

[21] I. Svalina, V. Galzina, R. Lujić, and G. Šimunović, “An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices,” Expert Systems with Applications, vol. 40, no. 15, pp. 6055–6063, Nov. 2013.

[22] H. Fattahi, A. Agah, and N. Soleimanpourmoghadam, “Multi-output adaptive neuro-fuzzy inference system for prediction of dissolved metal levels in acid rock drainage: a case study,” Journal of AI and Data Mining, vol. 6, no. 1, pp. 121–132, Mar. 2018.

[23] S. Sun, Y. Sun, S. Wang, and Y. Wei, “Interval decomposition ensemble approach for crude oil price forecasting,” Energy Economics, vol. 76, pp. 274–287, Oct. 2018.

[24] L. A. Aloo, P. K. Kihato, S. I. Kamau, and R. S. Orenge, “Modeling and control of a photovoltaic-wind hybrid microgrid system using GA-ANFIS,” Heliyon, vol. 9, no. 4, p. e14678, Apr. 2023.

[25] P. Dhal and C. Azad, “A comprehensive survey on feature selection in the various fields of machine learning,” Applied Intelligence, vol. 52, no. 4, pp. 4543–4581, Mar. 2022.

[26] J. Li et al., “Feature selection: a data perspective,” ACM Computing Surveys., vol. 50, no. 6, p. 94:1-94:45, Dec. 2017.

[27] G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Computers & Electrical Engineering, vol. 40, no. 1, pp. 16–28, Jan. 2014.
[28] B. Venkatesh and J. Anuradha, “A review of feature selection and its methods,” Cybernetics and Information Technologies, vol. 19, no. 1, pp. 3–26, Mar. 2019.

[29] R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, “A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction,” Journal of Applied Science and Technology Trends, vol. 1, no. 2, Art. no. 2, May 2020.

[30] L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, Jun. 1965.

[31] J. M. Mendel and R. I. B. John, “Type-2 fuzzy sets made simple,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 117–127, Apr. 2002.

[32] D. Ramot, R. Milo, M. Friedman, and A. Kandel, “Complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 171–186, Apr. 2002.

[33] C. Li and C.-H. Tu, “Complex neural fuzzy system and its application on multi-class prediction — A novel approach using complex fuzzy sets, IIM and multi-swarm learning,” Applied Soft Computing, vol. 84, p. 105735, Nov. 2019.

[34] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-15, no. 1, pp. 116–132, Jan. 1985.

[35] A. PRIYONO, M. Ridwan, A. ALIAS, R. Rahmat, A. Hassan, and M. Mohd Ali, “Generation of fuzzy rules with subtractive clustering,” Jurnal Teknologi, vol. 43, p. 143, Feb. 2005.

[36] M. Al-Mahasneh, M. Aljarrah, T. Rababah, and M. Alu’datt, “Application of hybrid neural fuzzy system (anfis) in food processing and technology,” Food Engineering Reviews, vol. 8, no. 3, pp. 351–366, Sep. 2016.

[37] M. Stojčić, “Application of ANFIS model in road traffic and transportation: a literature review from 1993 to 2018,” Operational Research in Engineering Sciences: Theory and Applications, vol. 1, no. 1, Art. no. 1, Dec. 2018.

[38] A. F. Cabalar, A. Cevik, and C. Gokceoglu, “Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering,” Computers and Geotechnics, vol. 40, pp. 14–33, Mar. 2012.

[39] A. Bagheri, H. Mohammadi Peyhani, and M. Akbari, “Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization,” Expert Systems with Applications, vol. 41, no. 14, pp. 6235–6250, Oct. 2014.

[40] J.-S. R. Jang, Neuro-Fuzzy And Soft Computing A Computational Approach To Learning And Machine Intelligence. Prentice Hall of India, 2008.

[41] K. Bataineh, M. Naji, and M. Saqer, “A comparison study between various fuzzy clustering algorithms,” Jordan Journal of Mechanical and Industrial Engineering, vol. 5, pp. 335–343, Jan. 2011.

[42] G. Venter, “Review of optimization techniques,” in Encyclopedia of Aerospace Engineering, 1st ed., R. Blockley and W. Shyy, Eds., Wiley, 2010.

[43] S. Körkel, H. Qu, G. Rücker, and S. Sager, “Derivative based vs. derivative free optimization methods for nonlinear optimum experimental design,” in Current Trends in High Performance Computing and Its Applications, W. Zhang, W. Tong, Z. Chen, and R. Glowinski, Eds., Berlin, Heidelberg: Springer, 2005, pp. 339–344.

[44] C. Li and P.-L. Wang, “A study on the gaussian distribution based whale optimization algorithm for optimization problems,” Journal of e-Business, vol. 25, no. 1, pp. 89–126, Apr. 2023.

[45] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, Art. no. 6088, Oct. 1986.

[46] S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, May 2016.

[47] T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, and A. Cosar, “A survey on new generation metaheuristic algorithms,” Computers & Industrial Engineering, vol. 137, p. 106040, Nov. 2019.

[48] J. J. Grefenstette, “Genetic algorithms and machine learning,” in Proceedings of the sixth annual conference on Computational learning theory - COLT ’93, Santa Cruz, California, United States: ACM Press, 1993, pp. 3–4.

[49] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, Jan. 1995, pp. 1942–1948 vol.4.

[50] M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, Jan. 2006.

[51] K. Hussain, M. N. Mohd Salleh, S. Cheng, and Y. Shi, “Metaheuristic research: a comprehensive survey,” Artificial Intelligence Review, vol. 52, no. 4, pp. 2191–2233, Dec. 2019.

[52] D. Karaboga, “An idea based on honey bee swarm for numerical optimization, technical report - tr06,” Technical Report, Erciyes University, Jan. 2005.

[53] X.-S. Yang, “Firefly algorithms for multimodal optimization,” in Stochastic Algorithms: Foundations and Applications, O. Watanabe and T. Zeugmann, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2009, pp. 169–178.
[54] G. R. Raidl, J. Puchinger, and C. Blum, “Metaheuristic hybrids,” in Handbook of Metaheuristics, M. Gendreau and J.-Y. Potvin, Eds., in International Series in Operations Research & Management Science. , Cham: Springer International Publishing, 2019, pp. 385–417.

[55] S. Paryani, A. Neshat, S. Javadi, and B. Pradhan, “Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping,” Natural Hazards, vol. 103, no. 2, pp. 1961–1988, Sep. 2020.

[56] C. Liang, K. Pan, M. Zhao, and M. Lu, “Multi-node path planning of electric tractor based on improved whale optimization algorithm and ant colony algorithm,” Agriculture, vol. 13, no. 3, Art. no. 3, Mar. 2023.

[57] Z.-X. Chen and H. Huang, “A hybrid method for intrusion detection with ga-based feature selection,” Intelligent Automation & Soft Computing, vol. 17, no. 2, pp. 175–186, Jan. 2011.

[58] X. Zhong and D. Enke, “Predicting the daily return direction of the stock market using hybrid machine learning algorithms,” Financial Innovation, vol. 5, no. 1, p. 24, Jun. 2019.

[59] C.-F. Huang, “A hybrid stock selection model using genetic algorithms and support vector regression,” Applied Soft Computing, vol. 12, no. 2, pp. 807–818, Feb. 2012.

[60] C.-H. Tu and C. Li, “Multitarget prediction—A new approach using sphere complex fuzzy sets,” Engineering Applications of Artificial Intelligence, vol. 79, pp. 45–57, Mar. 2019.

[61] S. Chiu, “Fuzzy model identification based on cluster estimation,” Journal of the Intelligent and Fuzzy Systems, vol. 2, pp. 267–278, Jan. 1994.

[62] K. Socha and M. Dorigo, “Ant colony optimization for continuous domains,” European Journal of Operational Research, vol. 185, no. 3, pp. 1155–1173, Mar. 2008.

[63] E.-G. Talbi, “A taxonomy of hybrid metaheuristics,” Journal of Heuristics, vol. 8, no. 5, pp. 541–564, Sep. 2002.

[64] K. A. De Jong, “An analysis of the behavior of a class of genetic adaptive systems.,” phd, University of Michigan, USA, 1975.

[65] G. C. Goodwin and K. S. Sin, Adaptive Filtering Prediction and Control. Courier Corporation, 2014.

[66] L. Ljung, System Identification: Theory for the User. Prentice Hall PTR, 1999.

[67] P. Strobach, Linear Prediction Theory: A Mathematical Basis for Adaptive Systems. Springer Science & Business Media, 2012.

[68] M. M. Kumbure, C. Lohrmann, P. Luukka, and J. Porras, “Machine learning techniques and data for stock market forecasting: a literature review,” Expert Systems with Applications, vol. 197, p. 116659, Jul. 2022.

[69] K. Huarng and T. H.-K. Yu, “Ratio-based lengths of intervals to improve fuzzy time series forecasting,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 36, no. 2, pp. 328–340, Apr. 2006.

[70] K.-H. Huarng, T. H.-K. Yu, and Y. W. Hsu, “A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 4, pp. 836–846, Aug. 2007.

[71] L.-Y. Hsu et al., “Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques,” Expert Systems with Applications, vol. 37, no. 4, pp. 2756–2770, Apr. 2010.

[72] S.-M. Chen and K. Tanuwijaya, “Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques,” Expert Systems with Applications, vol. 38, no. 12, pp. 15425–15437, Nov. 2011.

[73] C. H. Aladag, U. Yolcu, E. Egrioglu, and E. Bas, “Fuzzy lagged variable selection in fuzzy time series with genetic algorithms,” Applied Soft Computing, vol. 22, pp. 465–473, Sep. 2014.

[74] S. Askari, N. Montazerin, and M. H. F. Zarandi, “A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables,” Applied Soft Computing, vol. 35, pp. 151–160, Oct. 2015.

[75] O. Cagcag Yolcu, U. Yolcu, E. Egrioglu, and C. H. Aladag, “High order fuzzy time series forecasting method based on an intersection operation,” Applied Mathematical Modelling, vol. 40, no. 19, pp. 8750–8765, Oct. 2016.

[76] Y. Wang, Y. Lei, X. Fan, and Y. Wang, “Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning,” Mathematical Problems in Engineering, vol. 2016, pp. 1–12, Jan. 2016.

[77] Y. Wan and Y.-W. Si, “Adaptive neuro fuzzy inference system for chart pattern matching in financial time series,” Applied Soft Computing, vol. 57, pp. 1–18, Aug. 2017.

[78] C.-H. Cheng and J.-H. Yang, “Fuzzy time-series model based on rough set rule induction for forecasting stock price,” Neurocomputing, vol. 302, pp. 33–45, Aug. 2018.

[79] H. Wu, H. Long, and J. Jiang, “Handling forecasting problems based on fuzzy time series model and model error learning,” Applied Soft Computing, vol. 78, pp. 109–118, May 2019.

[80] Q. Dong and X. Ma, “Enhanced fuzzy time series forecasting model based on hesitant differential fuzzy sets and error learning,” Expert Systems with Applications, vol. 166, p. 114056, Mar. 2021.

[81] C. Li and T.-W. Chiang, “Complex neurofuzzy arima forecasting—a new approach using complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 3, pp. 567–584, Jun. 2013.

[82] Y. Wan and Y.-W. Si, “Adaptive neuro fuzzy inference system for chart pattern matching in financial time series,” Applied Soft Computing, vol. 57, pp. 1–18, Aug. 2017.

[83] Y. Ren, P. N. Suganthan, and N. Srikanth, “A novel empirical mode decomposition with support vector regression for wind speed forecasting,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1793–1798, Aug. 2016.

[84] C.-H. Cheng and J.-H. Yang, “Fuzzy time-series model based on rough set rule induction for forecasting stock price,” Neurocomputing, vol. 302, pp. 33–45, Aug. 2018.

[85] H. Guan, Z. Dai, S. Guan, and A. Zhao, “A neutrosophic forecasting model for time series based on first-order state and information entropy of high-order fluctuation,” Entropy, vol. 21, no. 5, Art. no. 5, May 2019.

[86] C.-H. Cheng, M.-C. Tsai, and C. Chang, “A time series model based on deep learning and integrated indicator selection method for forecasting stock prices and evaluating trading profits,” Systems, vol. 10, no. 6, Art. no. 6, Dec. 2022.

[87] S. Guan and A. Zhao, “A two-factor autoregressive moving average model based on fuzzy fluctuation logical relationships,” Symmetry, vol. 9, no. 10, Art. no. 10, Oct. 2017.

[88] H. Guan, Z. Dai, A. Zhao, and J. He, “A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network,” PLOS ONE, vol. 13, no. 2, p. e0192366, Feb. 2018.

指導教授

李俊賢(Chunshien Li)

審核日期

2024-7-18

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