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姓名 侯夆霖(Feng-Lin Hou)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 模糊類神經系統在時間序列上之預測與應用
(Neuro-Fuzzy System for Prediction and Application of Stock Price Index in Time Series)
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摘要(中) 面臨大數據時代,影響股票市場的各種因素使股票預測有些複雜和困難,準確的股票指數預測能幫助決策者採取正確的行動來發展更好的經濟。大多數傳統的時間序列模型在預測中只使用一個變數,多數使用收盤價格單一變數預測隔日收盤價格,而本研究所採用5個變數作為模型輸入,預測模型應該使用更多變數來提高預測的準確性,本研究提出一個以模糊類神經系統(Neuro-Fuzzy System, NFS)為架構,其結合Takagi-Sugeno模糊系統形成本研究模型,使其與傳統的類神經模型進行比較。在參數學習,以粒子群演算法(Particle Swarm Optimization, PSO)結合遞迴最小平方演算法(Recursive Least Squares Estimator, RLSE),成為PSO-RLSE複合型演算法,進行參數的優化,發揮效用。本研究以三個實驗使用多種真實世界驗證模型的效能與研究理論,實驗一為台股指數預測與利潤計算,實驗二為恆生指數預測與利潤計算,實驗三為日經指數預測與利潤計算,實驗結果說明本研究模型在時間序列預測上有良好效能。
摘要(英) Faced with the era of big data, various factors affecting the stock market make stock forecasting which are complicated and difficult. In order to obtain accurate stock index forecasts, we hope to help decision makers take the right actions to develop a better economy. Most traditional time series models use only one variable in the forecast. Most use the single variable of the closing price to predict the closing price of the next day. In this study, three variables are used as input to the model. The forecasting model should use more variables to improve the forecast. This study proposes a Neuro-Fuzzy System (NFS) architecture that combines the Takagi-Sugeno fuzzy system to form the model structure of this study, which is compared with the traditional neural network model. In the parameter learning, Particle Swarm Optimization (PSO) combined with Recursive Least Squares Estimator (RLSE) is used as a PSO-RLSE composite algorithm to optimize parameters. This study used a variety of real-world data sets to validate the model′s efficacy and research theory in three experiments. The results of individual experiments are compared with the previous literature. The research results show that the model has good performance in time series prediction.
關鍵字(中) ★ 模糊類神經系統
★ 粒子群演算法
★ 時間序列分析
★ 台股指數
★ 恆生指數
★ 日經指數
關鍵字(英) ★ Neural networks
★ Particle swarm optimization
★ Time series analysis
★ TAIEX
★ HSI
★ Nikkei
論文目次 章節
頁次
中文摘要 iv
英文摘要 ii
致謝 vi
目錄 vii
圖目錄 x
表目錄 xii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 研究方法概述 3
1.4 論文架構 4
第二章 文獻探討 6
2.1 股價分析理論 6
2.1.1 效率市場理論 6
2.1.2 股票價格分析法 7
2.2 股票市場上不同的預測模型 9
2.3 類神經模型 10
2.4 倒傳遞演算法 11
2.5 模糊模型 13
2.6 粒子群演算法 14
第三章 研究方法 16
3.1 模糊類神經模型 16
3.1.1 第一層 輸入層 18
3.1.2 第二層 模糊集合層 19
3.1.3 第三層 啟動強度層 19
3.1.4 第四層 正規化層 19
3.1.5 第五層 後鑑部層 19
3.1.6 第六層 輸出層 20
3.2 粒子群演算法 20
3.3 遞迴式最小平方演算法 21
3.4 應用型態 23
3.5 PSO-RLSE複合演算法 24
3.6 投資策略 27
第四章 實驗與研究結果 30
4.1 實驗一:台股加權指數時間序列預測 30
4.1.1 RMSE 比較 35
4.1.2 不同模型於文獻規則之利潤比較 36
4.1.3 不同模型於改良規則之利潤比較 39
4.2 實驗二:香港恆生指數時間序列預測 40
4.2.1 RMSE比較 45
4.2.2 不同模型於文獻規則之利潤比較 46
4.2.3 不同模型於改良規則之利潤比較 48
4.3 實驗三:日本日經指數時間序列預測 50
4.3.1 RMSE 比較 55
4.3.2 不同模型於文獻規則之利潤比較 56
4.3.3 不同模型於改良規則之利潤比較 59
第五章 討論 61
第六章 結論與未來研究方向 65
6.1 結論 65
6.2 未來研究方向 66
參考文獻 68
參考文獻 [ 1 ] B. Publishing, “American Finance Association,” The Journal of Finance, vol. 32, no. 3, pp. 663–682, 2012.
[ 2 ] K. Huarng and T. H. K. Yu, “The application of neural networks to forecast fuzzy time series,” Physica A: Statistical Mechanics and Its Applications, vol. 363, no. 2, pp. 481–491, 2006.
[ 3 ] J. Haidt, “The new synthesis in moral psychology,” Science, vol. 316, no. 5827, pp. 998–1002, 2007.
[ 4 ] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
[ 5 ] F. Black and R. Litterman, “Global Portfolio Optimization,” Financial Analysts Journal, vol. 48, no. 5, pp. 28–43, 1992.
[ 6 ] B. Russell, “Vagueness,” Australasian Journal of Philosophy and Philosophy, vol.1, issue 2, pp.84–92, 1923.
[ 7 ] J. Kennedy and R.C. Eberhart, “Particle Swarm Optimization,” Fourth Neural Networks IEEE International Conference, vol. 4, pp. 1942-1948, 1995
[ 8 ] T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka, “Stock market prediction system with modular neural networks,” Neural Networks, pp. 1–6 vol.1, 1990.
[ 9 ] H. O. Wang, K. Tanaka, and M. Griffin, “Parallel distributed compensation of nonlinear systems by Takagi-Sugeno fuzzy model,” IEEE International Conference Fuzzy System, vol. 2, pp. 531–538, 1995.
[ 10 ] S. Thawornwong and D. Enke, “The adaptive selection of financial and economic variables for use with artificial neural networks,” Neurocomputing, vol. 56, no. 1–4, pp. 205–232, 2004.
[ 11 ] W. Schiffmann, M. Joost, and R. Werner, “Optimization of the Backpropagation Algorithm for Training Multilayer Perceptrons,” University of Koblenz Institute of Physic., pp. 1–36, 1994.
[ 12 ] M. Billah, S. Waheed, and A. Hanifa, “Predicting Closing Stock Price using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS): The Case of the Dhaka Stock Exchange,” Computer Applications, vol. 129, no. 11, pp. 975–8887, 2015.
[ 13 ] T. L. Chen, C. H. Cheng, and H. J. Teoh, “High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets,” Physica A: Statistical Mechanics and Its Application, vol. 387, no. 4, pp. 876–888, 2008.
[ 14] K. Kim and I. Han, “Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index,” Expert System with Applications, vol. 19, pp. 125–132, 2000.
[ 15] F. Rep, G. Publishers, H. Platz, D.- Berlin, and F. R. Germany, “Linear Prediction Theory , A Mathematical Basis for Adaptive Systems,” Springer Series in Information Sciences, vol. 21, no. 3, pp. 284–285, 1990.
[ 16 ] H. Dourra and P. Siy, “Investment using technical analysis and fuzzy logic,” Fuzzy Sets and Systems, vol. 127, no. 2, pp. 221–240, 2002.
[ 17 ] D. A. Hirshleifer, “Behavioral Finance,” Economic Perspectives, vol. 17, no. 1, pp. 83–104, 2015.
[ 18 ] T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307–327, 1986.
[ 19 ] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” Systems, Man and Cybernetics of IEEE , vol. SMC-15, no. 1, pp. 116–132, 1985.
[ 20] G. J. Klir and B. Yuan, “Fuzzy Sets and Fuzzy Logic,” Prentice Hall PTR, p. 574, 1995.
[ 21] R. F. Engle, “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” The Econometric Society, vol. 50, no. 4, p. 987, 1982.
[ 22] Chung-Ming Kuan and Tung Liu, “Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks,” Econometrics Applied, vol. 10, no. 4, pp. 347–364, 2016.
[ 23] O. I. Franksen, G. C. Goodwin, and K. S. Sin, “Mr . Babbage ’ s Secret . The Tale of a Cypher Adaptive Filtering, Prediction and Control,” pp. 616–618, 1995.
[ 24] K. J. Kim, “Financial time series forecasting using support vector machines,” Neurocomputing, vol. 55, no. 1–2, pp. 307–319, 2003.
[ 25] C. W. Reynolds, “Flocks, herds and schools: A distributed behavioral model,” Computer Graphics, vol. 21, no. 4, pp. 25–34, 1987.
[ 26] T. Takagi and M. Sugeno, “Derivation of Fuzzy Control Rules from Human Operator’s Control Actions,” IFAC Proceedings Volumes, vol. 16, no. 13, pp. 55–60, 1983.
[ 27] D. G. Dickinson, “Stock market integration and macroeconomic fundamentals: An empirical analysis, 1980-95,” Applied Financial Economics, vol. 10, no. 3, pp. 261–276, 2000.
[ 28] T. Hyup Roh, “Forecasting the volatility of stock price index,” Expert Systems with Applications, vol. 33, no. 4, pp. 916–922, 2007.
[ 29] K. Huarng, “Effective lengths of intervals to improve forecasting in fuzzy time series,” Fuzzy Sets and Systems, vol. 123, no. 3, pp. 387–394, 2001.
[ 30] J. R. Jang, “ANFIS : Adap tive-Ne twork-Based Fuzzy Inference System,” Systems, Man and Cybernetics of IEEE , vol. 23, no. 3, 1993.
[ 31] M. A. Boyacioglu and D. Avci, “An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange,” Expert Systems with Application, vol. 37, no. 12, pp. 7908–7912, 2010.
[ 32] M. J. Kim, S. H. Min, and I. Han, “An evolutionary approach to the combination of multiple classifiers to predict a stock price index,” Expert Systems with Applications, vol. 31, no. 2, pp. 241–247, 2006.
[ 33] C. Nikolopoulos and P. Fellrath, “A hybrid expert system for investment advising,” Expert Systems, vol. 11, no. 4, pp. 245–250, 1994.
[ 34] Chunshien Li and Wen-Wen Chen, “Adaptive Image Restoration - A Computational Intelligence Approach,” Journal of Information Management, vol. 19, no. 3, 2012.
[ 35] F. Heppner and U. Grenander, “A stochastic nonlinear model for coordinated bird flocks,” The ubiquity of chaos, no. December. pp. 233–238, 1990.
[ 36] H. J. Sadaei, R. Enayatifar, M. H. Lee, and M. Mahmud, “A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting,” Applied Soft Computing, vol. 40, pp. 132–149, 2016.
[ 37] W. H. Schiffmann and H. W. Geffers, “Adaptive control of dynamic systems by back propagation networks,” Neural Networks, vol. 6, no. 4, pp. 517–524, 1993.
[ 38] B. R. Marshall and R. H. Cahan, “Is technical analysis profitable on a stock market which has characteristics that suggest it may be inefficient?,” International Business and Finance, vol. 19, no. 3, pp. 384–398, 2005.
[ 39] T. H. K. Yu and K. H. Huarng, “A bivariate fuzzy time series model to forecast the TAIEX,” Expert Systems Application, vol. 34, no. 4, pp. 2945–2952, 2008.
[ 40] J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” Systems, Man and Cybernetics of IEEE, vol. 23, no. 3, pp. 665–685, 1993.
[ 41] J. K. Mantri, P. Gahan, and B. B. Nayak, “Artificial Neural Networks – an Application to Stock Market Volatility,” International Journal Engineering Science and Technology, vol. 2, no. 5, pp. 1451–1460, 2010.
[ 42] Y. Bing, J. K. Hao, and S. C. Zhang, “Stock Market Prediction Using Artificial Neural Networks,” Advanced Engineering Forum, vol. 6–7, pp. 1055–1060, 2012.
[ 43] D. Avramov, “Stock return predictability and model uncertainty,” Journal of Financial Economics, vol. 64, no. 3, pp. 423–458, 2002.
[ 44] J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” Journal of Computer Science, vol. 2, no. 1, pp. 1–8, 2011.
[ 45] K. Huarng and T.H.K. Yu, “Ratio-based lengths of intervals to improve fuzzy time series forecasting,” Systems, Man, and Cybernetics of IEEE, vol. 36, issue. 2, April 2006
[ 46] S.M. Chen and C.D. Chen, “Handling forecasting problems based on high-order fuzzy logical relationships,” Expert Systems with Applications, vol. 38, issue 4, pp. 3857-3864, 2011
[ 47] H.J. Sadaei and M.H. Lee, “Multilayer Stock Forecasting Model Using Fuzzy Time Series,” Journal of The Scientific World, vol. 2014, pp. 1-10, 2014
[ 48] C. H. Cheng, L. Y. Wei, and Y. S. Chen, “Fusion ANFIS models based on multi-stock volatility causality for TAIEX forecasting,” Neurocomputing, vol. 72, no. 16–18, pp. 3462–3468, 2009.
[ 49] L. Y. Wei, “A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting,” Applied Soft Computing, vol. 42, pp. 368–376, 2016.
[ 50] J. R. Chang, L. Y. Wei, and C. H. Cheng, “A hybrid ANFIS model based on AR and volatility for TAIEX forecasting,” Applied Soft Computing, vol. 11, no. 1, pp. 1388–1395, 2011.
[ 51] 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, 2018.
[52] C. H. Cheng, L. Y. Wei, J. W. Liu, and T. L. Chen, “OWA-based ANFIS model for TAIEX forecasting,” Economic Modelling, vol. 30, no. 1, pp. 442–448, 2013.
[ 53] H.K. Yu, “Weighted fuzzy time-series models for TAIEX forecasting,” Physica A: Statistical Mechanics and Its Applications, vol. 349, issues 3-4 pp. 609–624, 2005.
[ 54] S.M. Chen, “Forecasting enrollments based on fuzzy time-series,” Fuzzy Sets and Systems, vol. 81, issue 3, pp. 11–319, 1996.
[ 55] Y. Wan, Y.-W. Si, “Adaptive neuro fuzzy inference system for chart pattern matching in financial time series,” Applied Soft Computer, vol. 57, pp. 1-18, 2017.
[56] J.L. Elman, “Finding structure in time,” Cognitive Science, vol.14, pp.179-211, 1990.
[57] Eugene F. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance, vol. 25, pp383-417, 1970.
[58] Astrom K.J. and Wittenmark B., “Adaptive comtrol,” 2nd edition, Journal of Prentice Hall, 1994.
指導教授 李俊賢(Chun-Shien Li) 審核日期 2019-1-2
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