博碩士論文 etd-0805109-121651 詳細資訊


[回到前頁查詢結果 | 重新搜尋]

姓名 黃祺偉(Chi-wei Huang) 電子郵件信箱 kiwi@water.ee.nsysu.edu.tw
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 97學年第2學期
論文名稱(中) 多核心支援迴歸向量機應用於股價預測
論文名稱(英) A Multiple-Kernel Support Vector Regression
Approach for Stock Market Price Forecasting
檔案
  • etd-0805109-121651.pdf
  • 本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
    請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
    論文使用權限

    電子論文:校內校外完全公開

    論文語文/頁數 中文/60
    統計 本論文已被瀏覽 5063 次,被下載 2223 次
    摘要(中) 近年來,支援迴歸向量機已成功地被用來解決證券市場預測的問題。然而,支援迴歸向量機需要手動的調整核心函數的超參數。因此有學者提出多核心學習法來解決這類的問題,其中核心矩陣的權重向量與拉格朗日乘數常使用半正定規劃法來同時解得。但這類的演算法需要很大量的時間與空間,因此本論文提出一種結合最小序列優化法與梯度投影法的兩階段多核心學習演算法。
    根據本演算法,使用者可以合併多個不同的超參數而使整個系統預測效果得到改善並且不需事先指定超參數的設置,更避免了過去需反覆實驗才可得到適合的超參數。本論文使用台灣加權指數加以實證,實驗結果顯示本方法效果優於其它的方法。
    摘要(英) Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually the hyperparameters of the kernel functions. Multiple-kernel learning was developed to deal with this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously derived through semidefinite programming. However, the amount of time and space required is very demanding. We develop a two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method.
    By this algorithm, advantages from different hyperparameter settings can be combined and overall system performance can be improved. Besides, the user need not specify the hyperparameter settings in advance, and trial-and-error for determining appropriate hyperparameter settings can then be avoided. Experimental results, obtained by running on datasets taken from Taiwan Capitalization Weighted Stock Index, show that our method performs better than other methods.
    關鍵字(中)
  • 梯度投影法
  • 最小序列優化法
  • 多核心學習
  • 支援迴歸向量機
  • 證券市場預測
  • 關鍵字(英)
  • SMO
  • multiple-kernel learning
  • support vector regression
  • Stock market forecasting
  • gradient projection
  • 論文目次 摘要 i
    Abstract ii
    目錄 iii
    圖目錄 iv
    表目錄 v
    第一章 導論 1
    1.1 研究動機 1
    1.2 研究目的 4
    1.3 論文架構 5
    第二章 基礎理論 7
    2.1 支援向量機器 7
    2.1.1 線性分割 7
    2.1.2 硬性邊界支援向量機 9
    2.1.3 軟性邊界支援向量機 13
    2.1.4 以核心運算為基礎的支援向量機 17
    2.2 支援迴歸向量機 24
    第三章 研究方法 27
    3.1 多核心支援迴歸向量機 27
    3.2 兩階段多核心學習 29
    第四章 實驗結果 33
    4.1 實驗一SKSVR與MKSVR比較 33
    4.2 實驗二ARIMA、SKSVR與MKSVR比較 37
    4.3 實驗三FNN、SKSVR與MKSVR比較 42
    第五章 結論與未來研究方向 46
    5.1 結論 46
    5.2 未來研究方向 46
    參考文獻 47
    參考文獻 參考文獻
    [1] F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan, “Multiple kernel learning, conic duality, and the SMO algorithm,” in Proceedings of the 21th International Conference on Machine Learning, pp. 6-13, 2004.
    [2] K. P. Bennett, M. Momma, and M. J. Embrechts, “MARK: A boosting algorithm for heterogeneous kernel models,” in Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 24-31, 2002.
    [3] D. P. Bertsekas, Nonlinear Programming, Second Edition, Athena Scientific, Massachusetts, USA, 1999.
    [4] T. Bollerslev, “Generalized autoregressive conditional heteroscedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307-327, 1986.
    [5] G. E. P. Box and G. M. Jenkins, Time series analysis: Forecasting and control, 3rd Edition, Prentice Hall, Englewood Cliffs, 1994.
    [6] L. Cao and F. E. H. Tay, “Financial forecasting using support vector machines,” Neural Computing & Applications, vol. 10, no. 2, pp. 184-192, 2001.
    [7] L. 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, 2003.
    [8] D. G. Champernowne, “Sampling theory applied to autoregressive schemes,” Journal of the Royal Statistical Society: Series B, vol. 10, pp. 204-231, 1948.
    [9] P.-C. Chang and C.-H. Liu, “A TSK type fuzzy rule based system for stock price prediction,” Expert Systems with Application, vol. 34, no. 1, pp. 135-144, 2008.
    [10] O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Machine Learning, vol. 46, no. 1-3, pp. 131-159, January 2002.
    [11] K. Crammer, J. Keshet, and Y. Singer, “Kernel design using boosting,” S. Becker, S. Thrun, and K. Obermayer (Eds.), in Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, USA, vol. 15, pp. 537-544, 2003.
    [12] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, Cambridge, UK, 2000.
    [13] K. Duan, S. Keerthi, and A. N. Poo, “Evaluation of simple performance measures for tuning SVM hyperparameters,” Neurocomputing, vol. 51, pp. 41-59, 2003.
    [14] R. F. Engle, “Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation,” Econometrica, vol. 50, no. 4, pp. 987-1008, 1982.
    [15] P.-C. Fernando, A. A.-R. Julio, and G. Javier, “Estimating GARCH models using support vector machines,” Quantitative Finance, vol. 3, no. 3, pp. 163-172, 2003.
    [16] T. V. Gestel, J. A. K. Suykens, D. E. Baestaens, A. Lambrechts, G. Lanckriet, B. Vandaele, B. D. Moor, and J. Vandewalle, “Financial time series prediction using least squares support vector machines within the evidence framework,” IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 809-821, 2001.
    [17] M. Gonen and E. Alpaydin, “Localized multiple kernel learning,” in Proceedings of the 25th International Conference on Machine Learning, pp. 352-359, 2008.
    [18] J. V. Hansen and R. D. Nelson, “Neural networks and traditional time series methods: A synergistic combination in state economic forecasts,” IEEE Transactions on Neural Networks, vol. 8, no. 4, pp. 863-873, 1997.
    [19] J. A. Hartigan and M. A. Wong, “A K-means clustering algorithm,” Applied Statistics, vol. 28, pp. 100-108, 1979.
    [20] V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, MIT Press, Cambridge, MA, USA, 2001.
    [21] K. J. Kim and I. Han, “Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index,” Expert Systems with Application, vol. 19, no. 2, pp. 125-132, 2000.
    [22] J. T.-Y. Kwok, “The evidence framework applied to support vector machines,” IEEE Transactions on Neural Networks, vol. 11, no. 5, pp. 1162-1173, 2000.
    [23] 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, 2007.
    [24] G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” Journal of Machine Learning Research, vol. 5, pp. 27-72, 2004.
    [25] S.-K. Oh, W. Pedrycz, and H.-S. Park, “Genetically optimized fuzzy polynomial neural networks,” IEEE Transactions on Fuzzy Systems, vol. 14, no. 1, pp. 125-144, 2006.
    [26] S. Ong, A. J. Smola, and R. C. Williamson, “Learning the kernel with hyperkernels,” Journal of Machine Learning Research, vol. 6, pp. 1043-1071, 2006.
    [27] P.-F. Pai and C.-S. Lin, “A hybrid ARIMA and support vector machines model in stock price forecasting,” Omega: The International Journal of Management Science, vol. 33, no. 6, pp. 497-505, 2005.
    [28] J. C. Platt, “Fast training of support vector machines using sequential minimal optimization,” B. Scholkopf, C. J. C. Burges, and A. J. Smola (Eds.), in Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, USA, vol. 11, pp. 185-208,1999.
    [29] 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, 2008.
    [30] A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet, “More efficiency in multiple kernel learning,” in Proceedings of the 24th International Conference on Machine Learning, pp. 775-782, 2007.
    [31] A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet, “SimpleMKL,” Journal of Machine Learning Research, vol. 9, pp. 2491-2521, 2008.
    [32] S. Sonnenburg, G. Ratsch, C. Schafer, and B. Scholkopf, “Large scale multiple kernel learning,” Journal of Machine Learning Research, vol. 7, pp. 1531-1565, 2006.
    [33] M. Szafranski, Y. Grandvalet, and A. Rakotomamonjy, “Composite kernel learning,” in Proceedings of the 25th International Conference on Machine Learning, pp. 1040-1047, 2008.
    [34] F. E. H. Tay and L. Cao, “Application of support vector machines in financial time series forecasting,” Omega: The International Journal of Management Science, vol. 29, no. 4, pp. 309-317, 2001.
    [35] I. W.-H. Tsang and J. T.-Y. Kwok, “Efficient hyperkernel learning using second-order cone programming,” IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 48-58, 2006.
    [36] G. Valeriy and B. Supriya, “Support vector machine as an efficient framework for stock market volatility forecasting,” Computational Management Science, vol. 3, no. 2, pp. 147-160, 2006.
    [37] V. Vapnik, The Nature of Statistical Learning Theory, Springer Verlag, New York, USA, 1995.
    [38] Z. Wang, S. Chen, and T. Sun, “MultiK-MHKS: A novel multiple kernel learning algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 348-353, 2008.
    [39] H. Yang, L. Chan, and I. King, “Support vector machine regression for volatile stock market prediction,” in Proceedings of the 3rd International Conference on Intelligent Data Engineering and Automated Learning, pp. 391-396, 2002.
    [40] M. H. F. Zarandi, B. Rezaee, I. B. Turksen, and E. Neshat, “A type-2 fuzzy rule-based expert system model for stock price analysis,” Expert Systems with Application, vol. 36, no. 1, pp. 139-154, 2009.
    [41] D. Zhang and L. Zhou, “Discovering golden nuggets: Data mining in financial application,” IEEE Transactions on Systems, Man, and Cybernetics, Part C, Applications and Reviews, vol. 34, no. 4, pp. 513-522, 2004.
    [42] Taiwan Stock Exchange Corporation.[Online]. URL http://www.twse.com.tw/
    [43] 臺灣行政院金融監督管理委員會證卷期貨局.[Online]. URL    http://www.sfb.gov.tw/
    口試委員
  • 洪宗貝 - 召集委員
  • 林文揚 - 委員
  • 謝朝和 - 委員
  • 郭忠民 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2009-07-22 繳交日期 2009-08-05

    [回到前頁查詢結果 | 重新搜尋]


    如有任何問題請與論文審查小組聯繫