自我建構式複數模糊ARIMA於指數波動預測之研究

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鄭馨惠(Hsin-Hui Cheng) 查詢紙本館藏

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資訊管理學系

論文名稱

自我建構式複數模糊ARIMA於指數波動預測之研究
(Stock Index Volatility Forecasting― A Self-Organizing Approach Using Complex Fuzzy ARIMA Model)

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摘要(中)

本研究提出一個自我建構式複數模糊類神經系統（Self-organizing complex neuro-fuzzy system, SOCNFS），結合差分自回歸移動平均模型（Autoregressive integrated moving average, ARIMA），形成SOCNFS-ARIMA模型並應用於時間序列預測之研究。此智慧型系統之學習過程分成結構學習階段（Structure learning phase）與參數學習階段（Parameter learning phase）兩階段。結構學習階段使用SC-based FCM分群演算法，自動決定符合樣本資料分布特性的系統架構與大小；參數學習階段使用粒子群最佳化演算法（Particle swarm optimization, PSO）與遞迴式最小平方估計器（Recursive least squares estimator, RLSE），稱為PSO-RLSE複合式學習演算法，進行系統參數之快速學習。本研究使用複數模糊集合（Complex fuzzy sets, CFSs）與差分自回歸移動平均模型的概念，分別應用在模糊規則的前鑑部與後鑑部，以增加處理複雜系統問題之映射能力。本研究設計五個實驗來驗證系統效能：Mackey-Glass混沌時間序列與星星亮度時間序列是指標性的序列，運用本研究之結果和過去文獻比較，可以了解本研究模型之可信度；實驗三至實驗五取自真實世界資料，實驗三的標準普爾500指數日波動序列實驗驗證加入複數模糊集合與ARIMA模型的效能；實驗四與實驗五測試同步預測不同時間序列的效能，道瓊工業平均指數與那斯達克綜合指數年波動時間序列實驗亦驗證轉換波動之不同取樣區間對於預測性能的差異；而標準普爾500指數與上海證券交易所股票價格綜合指數年波動時間序列實驗則證實本研究模型擁有同時預測兩個不同經濟體系資料的能力。實驗結果顯示本研究模型在證券股價指數波動預測上有良好效能。

摘要(英)

A self-organizing intelligent approach, using complex fuzzy sets (CFSs), neuro-fuzzy theory, and autoregressive integrated moving average (ARIMA) model, is proposed to the problem of stock index volatility forecasting. The proposed computing system is called the self-organizing complex neuro-fuzzy system ARIMA (denoted as SOCNFS-ARIMA). A SC-based FCM clustering method, consisting of the subtractive clustering (SC) and the fuzzy c-means (FCM), is used to automatically determine the initial knowledge base of SOCNFS-ARIMA. The proposed approach has excellent functional mapping ability for prediction. The PSO-RLSE hybrid learning algorithm is proposed for the purpose of fast learning, integrating the particle swarm optimization (PSO) and the recursive least squares estimator (RLSE). To test the proposed approach, five experiments are conducted, including the Mackey-Glass chaos time series, the star brightness time series, the S&P 500 index, the Dow Jones Industrial Average (DJIA) index and the NASDAQ composite index, and the Shanghai Stock Exchange (SSE) composite index. According to the experimental results, for the first two experiments, the proposed approach has outperformed the compared approaches in the literature. The experimental results have exposed the merits of CFSs in the proposed approach and have shown successfully the practice of dual volatility prediction with excellent performance for two time series of stock market.

關鍵字(中)

★ PSO-RLSE複合式學習演算法
★ SC-based FCM分群演算法
★ 差分自回歸移動平均模型
★ 複數模糊類神經系統
★ 指數波動預測
★ 時間序列

關鍵字(英)

★ PSO-RLSE hybrid learning method
★ SC-based FCM clustering algorithm
★ autoregressive integrated moving average
★ complex neuro-fuzzy system (CNFS)
★ time series forecasting

論文目次

摘要　i
Abstract　ii
誌謝　iii
目錄　iv
圖目錄　v
表目錄　vii
論文之記號列表　viii
第一章　緒論　1
1.1 研究背景　1
1.2 研究動機與目的　2
1.3 研究方法概述　4
1.4 論文架構　5
第二章　研究方法　7
2.1 複數模糊集合　7
2.2 差分自回歸移動平均模型　9
2.3 自我建構式複數模糊類神經差分自回歸移動平均模型　12
第三章　學習策略　17
3.1 結構學習階段　17
3.2 參數學習階段　20
3.2.1 粒子群最佳化演算法　22
3.2.2 遞迴式最小平方估計器　23
3.2.3 PSO-RLSE複合式學習演算法　25
第四章　實驗　28
4.1 實驗一：Mackey-Glass混沌時間序列　34
4.2 實驗二：星星亮度時間序列　39
4.3 實驗三：標準普爾500指數日波動序列　45
4.4 實驗四：道瓊工業平均指數與那斯達克綜合指數年波動時間序列　52
4.5 實驗五：標準普爾500指數與上海證券交易所股票價格綜合指數年波動時間序列　59
第五章　討論　65
第六章　結論與未來研究方向　69
6.1 結論　69
6.2 未來研究方向　71
參考文獻　73

參考文獻

[1]　Abu-mostafa, Y. S. and Atiya, A. F., “Introduction to financial forecasting,” Applied Intelligence, vol. 6, pp. 205-213, 1996.
[2]　Arthur, S., Sheffrin and S. M., Economics: Principles in Action, Upper Saddle River, New Jersey, Pearson Prentice Hall, pp. 290, 2003.
[3]　Bailie, R. T. and DeGennaro, R. P., “Stock returns and volatility,” Journal of Financial and Quantitative Analysis, vol. 25, pp. 203-214, 1990.
[4]　Bezdek, J. C., Ehrlich, R. and Full, W., “FCM: the fuzzy c-means clustering algorithm,” Computers and Geosciences, vol. 10, pp. 191-203, 1984.
[5]　Black, F. and Scholes, M., “The pricing of options and corporate liabilities,” The Journal of Political Economy, vol. 81, no. 3, pp. 637-654, 1973.
[6]　Box, G. E. P., Jenkins, G. M. and Reinsel, G. C., Time Series Analysis: Forecasting and Control, Holden-day, San Francisco, CA, USA, 1976.
[7]　Brace, M. C., Bui-Nguyen, V. and Schmidt, J., “Another look at forecast accuracy of neural networks,” Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems, pp. 389-394, 1993.
[8]　Buckley, J. J., “Fuzzy complex numbers,” Fuzzy Sets and Systems, vol. 33, pp. 333-345, 1989.
[9]　Chen, P., Pedersen, T., Bak-Jensen, B. and Chen, Z., “ARIMA-based time series model of stochastic wind power generation,” IEEE Transactions on Power Systems, vol. 25, pp. 667-676, 2010.
[10]　Chen, Y., Yang, B. and Dong, J, “Time-series prediction using a local linear wavelet neural network,” Neurocomputing, vol. 69, pp. 449-465, 2006.
[11]　Deboeck, G. J., Trading on the edge: neural, genetic, and fuzzy systems for chaotic financial markets, New York, Wiley, 1994.
[12]　Denton, J., “How good are neural networks for causal forecasting,” Journal of Business Forecasting Methods and Systems, vol. 14, pp. 17-20, 1995.
[13]　Dhar, J., Agrawal, P., Singhal, V., Singh, A., Murmu and R. Kr., “Comparative study of volatility forecasting between ANN and hybrid models for Indian market,” International Research Journal of Finance and Economics, issue 45, pp. 68-79, 2010.
[14]　Dunn, J. C., “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” Journal of Cybernetics, vol. 3, pp. 32-57, 1973.
[15]　Ediger, V. S. and Akar, S., “ARIMA forecasting of primary energy demand by Fuel in Turkey,” Energy Policy, vol. 35, pp. 1701-1708, 2007.
[16]　Edwards, F. R., “Does futures trading increase stock market volatility,” Financial Analysts Journal, vol. 44, no. 1, pp. 63-69, 1998.
[17]　Engle, R. F., “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation,” Econornetrica, vol. 50, no. 4, pp.987-1007, 1982.
[18]　Esfahanipour, A. and Aghamiri, W., “Adapted neuro-fuzzy inference system on indirect approach TSK fuzzy rule base for stock market analysis,” Expert Systems with Applications, vol. 37, pp. 4742-4748, 2010.
[19]　Fong, B., Fong, A.C.M., Hong, G.Y. and Wong, L., “An empirical study of volatility predictions: stock market analysis using neural networks,” Lecture Notes in Computer Science, vol. 3828, pp. 473-480, 2005.
[20]　Fostera, W. R., Collopyb, F. and Ungar, L.H., “Neural network forecasting of short, noisy time series,” Computers and Chemical Engineering, vol. 16, pp. 293-297, 1992.
[21]　Freisleben, B. and Ripper, K., “Volatility estimation with a neural network,” Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering, pp. 177-181, 1997.
[22]　French, K. R., Schwert, G. W. and Stambaugh, R. F., “Expected stock returns and volatility,” Journal of Financial Economics, vol. 19, pp. 3-29, 1987.
[23]　Gao, Y. and Er, M. J., “NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approach,” Fuzzy sets and systems, vol. 150, pp. 331-350, 2005.
[24]　Graves, D. and Pedrycz, W., “Fuzzy prediction architecture using recurrent neural networks,” Neurocomputing, vol. 72, pp. 1668-1678, 2009.
[25]　Hamao, Y. R., Masculis, W. and Ng, V., “Correlation in price changes and volatility across international stock markets,” Review of Financial Studies, vol. 3, pp. 281-307, 1990.
[26]　Hamid, S. A. and Iqbal, Z., “Using neural networks for forecasting volatility of S&P 500 index futures prices,” Journal of Business Research, vol. 57, pp. 1116-1125, 2004.
[27]　Ho, S. L. and Xie, M., “The use of ARIMA models for reliability forecasting and analysis,” Computers & Industrial Engineering, vol. 35, pp. 213-216, 1998.
[28]　Hull, J. C., Options, Futures, and Other Derivatives, Prentice Hall, Pearson, University of Toronto, CA, 2011.
[29]　Jang, J. S. R., “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, pp. 665-668, 1993.
[30]　Jang, J. S. R., Sun, C. T. and Mizutani, E., Neuro-Fuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River, NJ, USA, 1997.
[31]　Jeng, J.-T., Chuang, C.-C. and Tao, C.-W., “Hybrid SVMR-GPR for modeling of chaotic time series systems with noise and outliers,” Neurocomputing, vol. 73, pp. 1686-1693, 2010.
[32]　Kawallwer, I. G., Koch, P. D. and Peterson, J. E., “Assessing the intraday relationship between implied and historical volatility,” Journal of Futures Markets, vol. 14, pp. 323-346, 1994.
[33]　Kayacan, E. and Kaynak, O., “Single-step ahead prediction based on the principle of concatenation using grey predictors,” Expert Systems with Applications, vol. 38, pp. 9499-9505, 2011.
[34]　Kennedy, J. and Eberhart, R., “Particle swarm optimization,” IEEE International Joint Conference on Neural Networks, vol. 4, pp. 1942-1948, 1995.
[35]　Leu, Y., Lee, C. P. and Hung, C. C., “A fuzzy time series-based neural network approach to option price forecasting,” Asian Conference on Intelligent Information and Database Systems 2010, Part I, LNAI vol. 5990, pp. 360-369, 2010.
[36]　Li, W. and Yang, Y., “Hybrid kernel learning via genetic optimization for TS fuzzy system identification,” International Journal of Adaptive Control and Signal Processing, vol. 24, pp. 12-19, 2010.
[37]　Luna, I., Soares, S. and Ballini, R, “A constructive-fuzzy system modeling for time series forecasting,” IEEE International Joint Conference on Neural Networks, vol. 16, pp. 2908-2913., 2007.
[38]　Mackey, M .C. and Glass, L., “Oscillation and chaos in physiological control systems,” Science, vol. 197, pp. 287-289, 1977.
[39]　MacQueen, J. B., “Some methods for classification and analysis of multivariate observations,” Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, pp. 281–297, 1967.
[40]　Malliaris, M. and Salchenberger, L., “Using neural networks to forecast the S&P 100 implied volatility,” Neurocomputing, vol. 10, pp. 183-195, 1996.
[41]　Mantri, J. K. and Gahan, P., “Volatility estimation using extreme-value-estimators & MLP model,” International Journal of Computer Applications, col. 10, no. 6, pp. 1-4,2010.
[42]　Pai, P.-F. and Lin, C.-S., “A hybrid ARIMA and support vector machines model in stock price forecasting,” Omega, vol. 33, pp. 497-505, 2005.
[43]　Poon, S.-H. and Granger, C.W. J., “Forecasting volatility in financial markets: a review,” Journal of Economic Literature, vol. 41, pp. 478-539. 2003.
[44]　Ramot, D., Friedman, M., Langholz, G. and Kandel, A., “Complex fuzzy logic,” IEEE Transactions on Fuzzy Systems, vol. 11, pp. 450-461, 2003.
[45]　Ramot, D., Milo, R., Friedman, M. and Kandel, A., “Complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 10, pp. 171-186, 2002.
[46]　Rojas, I., Valenzuela, O., Rojas, F., Guillen, A., Herrera, L.J., Pomares, H., Marquez, L. and Pasadas, M., “Soft-computing techniques and ARMA model for time series prediction,” Neurocomputing, vol. 71, pp. 519-537, 2008.
[47]　Tan, M.-C., Wong, S. C., Xu, J.-M., Guan, Z.-R. and Zhang, P., “An aggregation approach to short-term traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 10, pp. 60-69, 2009.
[48]　Taskaya-Temizel, T. and Casey, M. C., “A comparative study of autoregressive neural network hybrids,” Neural Networks, vol. 18, pp. 781-789, 2005.
[49]　Valdés, J. J., “Time series models discovery with similarity-based neuro-fuzzy networks and evolutionary algorithms,” Proceedings of the IEEE World Congress on Computational Intelligence, pp. 2345-2350, 2002.
[50]　Wong, W. K., Xia, M. and Chu, W. C., “Adaptive neural network model for time-series forecasting,” European Journal of Operational Research, vol. 207, pp. 807-816, 2010.
[51]　Yager, R. R. and Filev, D. P., “Approximate clustering via the mountain method,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 24, pp. 1279-1284, 1994.
[52]　Yahoo! Finance, http://finance.yahoo.com/.
[53]　Zhang, G. P., “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159-175, 2003.
[54]　Zhao, L. and Yang, Y. P, “PSO-based single multiplicative neuron model for time series prediction,” Expert Systems with Applications, vol. 36, pp. 2805-2912, 2009.
[55]　Zounemat-Kermani, M. and Teshnehlab, M, “Using adaptive neuro-fuzzy inference system for hydrological time series prediction,” Applied Soft Computing, vol. 8, pp. 928-936, 2008.

指導教授

李俊賢(Chunshien Li)

審核日期

2011-7-19

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