簡易檢索 / 詳目顯示

回結果列表
研究生： 吳永標
Yung-Piao Wu
論文名稱： 基於聚類與循序特徵分析的股票趨勢分析系統
Stock Trend Prediction by Sequential Chart Pattern via K-Means and AprioriAll Algorithm
指導教授： 李漢銘
Hahn-Ming Lee
口試委員: 林豐澤
none
鄭博仁
none
李育杰
none
鮑興國
none
學位類別： 碩士
Master
系所名稱： 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 101
中文關鍵詞： 趨勢預測 、聚類 、小波轉換 、循序特徵序列
外文關鍵詞： Haar wavelet, AprioriAll, Sequential Chart Pattern
相關次數： 點閱：296  下載：9 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  •  上一筆

    • 進行這個研究的主要想法是認為股市預測中循序特徵(Sequential pattern)是會重複發生的，因此使用尋找時間軸的循序特徵進行股市預測是個很重要的課題，循序特徵有很多處理的方法，在這個研究中，我們將股票線型採用K-Means聚類與AprioriAll循序特徵分析的方法進行台灣加權指數(TAIEX)趨勢的預測，以作為交易的參考依據。預測採用下列的步驟方法進行預測:(1) Chart Extractor模組，將指數金融序列線型取固定的Sliding Windows Size切割成多個序列的線型經過使用小波轉換(Haar Wavelet Transfer) 去除高頻的Noise，減少資料的維度，取得轉換後的平滑過線型 (2) 再經過Chart Recognition模組，採用相關系數(Correlation Coefficient)演算法針對線型進行相關度的分析 (3)使用K-Means演算法針對所有的平滑過的線型進行聚類，減少資料的複雜度，聚類的分群數量由給定的聚類間相關系數自動判定 (4) 使用AprioriAll演算法找出聚類的循序特徵 (5) 趨勢分析系統透過找出的循序聚類，經過歷史資料的統計結果作為未來趨勢的預測 (6) 最後設定交易的策略，以獲得最大利益。經過實驗證實使用聚類與循序特徵分析系統進行股票趨勢分析其效果是有效的。

    This paper investigates stock market investment issues on stock market we present a model to predict the stock trend based on STPS (Stock Trend Prediction by Sequential chart Pattern via K-Means and AprioriAll Algorithm). Predict method follow operations are performed : (1) the Chart Pattern Extractor by sliding window size and Haar wavelet transfer (2) the Chart Recognition by correlation coefficient (3) Chart Pattern Clustering by K-Means algorithm (4) Sequential Clustering Chart Pattern by AprioriAll Algorithm (5) Trend Analysis by statistic sequential clustering average index return and up/down days ratio (6) Trading strategy. That results in our proposed method to do the stock prediction by chart pattern sequence via K-Means and AprioriAll algorithm for real time market is effective and obtain better performance; as indicated form experiment results.

    Abstract I Acknowledgements VI Contents VIII List of Figures XI List of Tables XIII Chapter 1 Introduction 11 1.1 Stock Trend Prediction 12 1.2 The Challenges of Stock Trend Prediction 14 1.3 Motivations 15 1.4 Goals 15 1.5 The Outline of thesis 16 Chapter 2 Background and Related Work 17 2.1 The Stock Trend Prediction Techniques 18 2.1.1 Association Rule based Approaches 18 2.1.2 Chart Pattern Recognition based Approaches 19 2.1.3 Template-Matching technique based Approaches 20 2.1.4 Genetic Algorithm Technique based Approaches 21 2.1.5 Support vector machines (SVM) based Approaches 21 2.1.6 Neural Network based Approaches 22 2.1.7 Fuzzy based Approaches 23 2.2 Recognition Methodology 24 2.2.1 Discrete Wavelet transform (DWT) 24 2.2.2 Correlation Coefficient 24 2.3 Clustering Methodology 25 2.4 Association Rules 26 2.4.1 Apriori Algorithm Technique 27 2.4.2 AprioriAll Algorithm Technique 28 Chapter 3 Stock Trend Prediction by Sequential Chart Pattern 29 3.1 The Concept of Stock Trend Prediction System 30 3.2 The System Architecture of Stock Trend Prediction System 30 3.3 The Chart Extractor 32 3.4 The Chart Recognition Analyzer 35 3.5 The Chart Clustering Analyzer 38 3.6 The Sequential Chart Pattern Finder 42 3.7 Stock Trend Analysis 45 3.8 The Trading Strategy 46 Chapter 4 Experiments 48 4.1 Description of Data Set 49 4.2 Evaluation Design 49 4.2.1 Experiment 1 : Similarity Parameters Analysis 50 4.2.1.1 Results and Discussion 51 4.2.2 Experiment 2 : Training Period Analysis 56 4.2.2.1 Results and Discussion 56 4.2.3 Experiment 3 : Sliding Window Analysis 58 4.2.3.1 Results and Discussion 59 4.2.4 Experiment 4 : The Parameters for AprioriAll Algorithm Analysis 61 4.2.4.1 Results and Discussion 62 4.3 Experiment 5 : Comparison with Other Method 68 4.3.1 Results and Discussion 69 4.4 Experiment 6 : Comparison with mutual funds 70 4.4.1 Results and Discussion 71 4.5 The Summary 85 Chapter 5 Conclusion and Further Work 87 5.1 Discussion 88 5.1.1 Stock Trend impact factors 88 5.2 Conclusion 89 5.3 Further Work 91 References 93 Vita 99

    [1] E. F. Fama, "The Behavior of Stock-Market Prices," The Journal of Business, vol. 38, No 1., pp. 34-105, Jan. 1965.
    [2] D. S. Lee, "Adaptive switching on model selection of financial time series," Master's Thesis, 2005.
    [3] P. K. Padhiary and A. P. Mishra, "Development of Improved Artificial Neural Network Model for Stock Market Prediction," International Journal of Engineering Science and Technology (IJEST), vol. 3 No. 2, 2 Feb 2011.
    [4] T.-c. Fu, "A Review on Time Series Data Mining," Engineering Applications of Artificial Intelligence, vol. 24, pp. 164-181, February 2011.
    [5] G. Atsalakis and K. Valavanis, "Surveying Stock Market Forecasting Techniques – Part II: Soft Computing Methods," presented at the Expert Systems with Applications 36, 2009.
    [6] J. Han and M. Kamber. (2006). Data Mining: Concepts and Techniques, 2nd ed.
    [7] T. Brijs, G. Swinnen, K. Vanhoof, and G. Wets, "Building an Association Rules Framework to Improve Product Assortment Decisions," Data Mining and Knowledge Discovery, vol. 8, pp. 7-23, 2004.
    [8] D. P. Gandhmal, R. B. Parihar, and R. V. Argiddi, "An Optimized Approach to Analyze Stock market using Data Mining Technique," IJCT, ICETT, 2011.
    [9] C.-H. Park and S. H. Irwin, "What do you know about the Return ability of technical analysis?," Journal of Economic Surveys, vol. 21, pp. 786-826, September 2007.
    [10] A. W. Lo, H. Mamaysky, and J. Wang, "Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation.," The Journal of Finance, vol. 55, pp. 1705-1765, 2000.
    [11] E. Hajizadeh, H. D. Ardakani, and J. Shahrabi, "Application of data mining techniques in stock markets: A survey," Journal of Economics and International Finance, vol. 2(7), pp. 109-118, July 2010.
    [12] T. L. Chen, "Forecasting the Taiwan stock market with a stock trend recognition model based on the characteristic matrix of a bull market," African Journal of Business Management vol. 5(23), pp. 9947-9960, 7 October 2011.
    [13] S.-H. Liao, H.-h. Ho, and H.-w. Lin, "Mining stock category association and cluster on Taiwan stock market," Expert Systems with Applications, vol. 35, pp. 19–29, 2008.
    [14] N. A. Gershenfeld and A. S. Weigend, "The Future of Time Series" In: Time Series Prediction: Forecasting the Future and Understanding the Past, A. S. Weigend and N. A. Gershenfeld, eds., Addison-Wesley, 1993.
    [15] H. Lu, J. Ha, and L. Feng, "Stock Movement Prediction And N-Dimensional Inter-Transaction Association Rules " in Proc. 1998 SIGMOD Workshop Research Issues on Data Mining and Knowledge Discovery (DMKD'98), Seattle, WA, 1998, pp. 12:1-12:7.
    [16] R. V. Argiddi and S. S. Apte, "Future Trend Prediction of Indian IT Stock Market Using Association Rule Mining of Transaction data," International Journal of Computer Application, vol. 39, pp. 0975-8887, February 2012.
    [17] R. V. Argiddi and S. S. Apte, "Fragment Based Approach to Forecast Association Rules from Indian IT Stock Transaction Data," (IJCSIT) International Journal of Computer Science and Information Technologies, vol. 3 (2), pp. 3493-3497, 2012.
    [18] T. N. Bulkowski, Encyclopedia of Chart Patterns: John Wiley & Sons, Inc., 2000.
    [19] T. N. Bulkowski, Trading Classic Chart Patterns: John Wiley & Sons, 2002.
    [20] W. Leigh, N. Modani, R. Purvis, and T. Roberts, "Stock market trading rule discovery using technical charting heuristics," Expert Systems with Applications, vol. 23, pp. 155-159, August 2002.
    [21] J.-L. Wang and S.-H. Chan, "Stock market trading rule discovery using pattern recognition and technical analysis," Expert Systems with Applications, vol. 33, pp. 304-315, 2007.
    [22] W. Leigha, M. Pazb, and R. Purvisc, "An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index," Omega, vol. 30, pp. 69-76, 2002.
    [23] W. Leigh, R. Purvis, and J. M. Ragusa, "Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support," Decision Support Systems, vol. 32, pp. 361-377, March 2002.
    [24] W. Leigh, N. Paz, and R. Purvis, "Market timing: a test of a charting heuristic," Economics Letters, vol. 77, pp. 55-63, September 2002.
    [25] William Leigh, C. J. Frohlich, S. Hornik, R. L. Purvis, and T. L. Roberts, "Trading With a Stock Chart Heuristic," IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, vol. 38, pp. 93-104, Jan. 2008.
    [26] P. Parracho, R. Neves, and N. Horta, "Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel," presented at the Evolutionary Computation (CEC), 2011 IEEE Congress on, 2011.
    [27] N. E. E. Savin, P. A. Weller, and J. Zvingelis, "The Predictive Power of 'Head-and-Shoulders' Price Patterns in the U.S. Stock Market," Journal of Financial Econometrics, vol. 5, No 2, pp. 243-265, 2007.
    [28] A. Esfahanipour and S. Mousavi, "A genetic programming model to generate risk-adjusted technical trading rules in stock markets," Expert Systems with Applications 38, pp. 8438-8445, 2011.
    [29] C.-H. L. Lee, Y.-C. Liaw, and L. Hsu, "Investment decision making by using fuzzy candlestick pattern and genetic algorithm," presented at the 2011 IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, 2011.
    [30] P.-W. Chen, J.-Y. Wang, and H.-M. Lee, "Model Selection of SVMs Using GA Approach," Neural Networks, Proceedings IEEE International Joint Conference on, 25-29 July 2004.
    [31] B. Bernhard E, G. Isabelle M, and V. N. Vapnik, "A Training Algorithm for Optimal Margin Classifiers," Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, ACM Press, 1992.
    [32] K.-j. Kim, "Financial time series forecasting using support vector machines," Neurocomputing 55, pp. 307-319, 2003.
    [33] S. Abe, Support Vector Machines for Pattern Classification: Springer, 2005.
    [34] J. Wang, X. Wu, and C. Zhang, "Support vector machines based on K-means clustering for real-time business intelligence systems," Int. J. Business Intelligence and Data Mining, vol. 1, No 1, 2005.
    [35] G. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art," International Journal of Forecasting 14, pp. 35-62, 1998.
    [36] K. J. Oh and K.-j. Kim, "Analyzing stock market tick data using piecewise non linear model. Expert System with Applications," Expert System with Applications, 22, pp. 249-255, 2002.
    [37] M. Jasemia, A. M. Kimiagaria, and A. Memarianib, "A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick," Expert Systems with Applications, vol. 38, pp. 3884-3890, April 2011.
    [38] S.-T. Li and S.-C. Kuo, "Knowledge discovery in financial investment for forecasting and trading strategy through wavelet-based SOM networks," Expert Systems with Applications 34, pp. 935-951, 2008.
    [39] W. Li-Xin, A Course in Fuzzy Systems and Control: Prentice-Hall, Inc., New Jersey, 1997.
    [40] C. Quek, Z. Guo, and D. L. Maskell, "A Novel Fuzzy Associative Memory Architecture for Stock Market Prediction and Trading," International Journal of Fuzzy System Applications, vol. 1(1), pp. 61-78, January-March 2011.
    [41] G. S. Atsalakis, E. M. Dimitrakakis, and C. D. Zopounidis, "Elliott Wave Theory and neuro-fuzzy systems, in stock market prediction: The WASP system," Expert Systems with Applications, vol. 38, pp. 9196-9206, August 2011.
    [42] 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, pp. 1348-1358, April 2012.
    [43] E. Hadavandia, H. Shavandia, and A. Ghanbarib, "Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting," Knowledge-Based Systems, vol. 23, pp. 800-808, December 2010.
    [44] R. Agrawal, C. Faloutsos, and A. Swami, "Efficient similarity search in sequence databases," IBM Almaden Research Center, San Jose1993.
    [45] K. P. Chan and A. W. c. Fu, "Efficient time series matching by wavelets," in Proceedings of the 1st IEEE International Conference on Data Engineering, 1999, pp. 126-133.
    [46] F. K.-P. Chan, A. W.-c. Fu, and C. Yu, "Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping,," IEEE Transactions on Knowledge and Data Engineering, pp. 686-705, May/June 2003
    [47] J. L. Rodgers and W. A. Nicewander, "Thirteen Ways to Look at the Correlation Coefficient," The American Statistician, vol. 42, pp. 59-66, Feb. 1988.
    [48] J. B. MacQueen, "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability," Berkeley, University of California Press, pp. 1:281-297, 1967.
    [49] H. He, J. Chen, H. Jin, and S. Chen, "Stock Trend Analysis and Trading Strategy," in JCIS. Atlantis Press, 2006.
    [50] R. Agrawal, T. Imielinski, and A. Swami, "Mining association rules between sets of items in large databases," in Proceedings of the ACM SIGMOID Conference on Management of Data, Washington, D.C, USA, 1993 pp. 207-216.
    [51] R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and I. and Verkamo, "Fast discovery of association rules," in In Advances in Knowledge Discovery and Data Mining, 1996, pp. 307-328.
    [52] R. Agrawal and R. Srikant, "Mining Sequential Patterns," in Data Engineering, Proceedings of the Eleventh International Conference on, 1995, pp. 3-14.
    [53] W. Tong and H. Pi-lian, "Web Log Mining by an Improved AprioriAll Algorithm," World Academy of Science, Engineering and Technology, 4 2005.
    [54] F. Masseglia, P. Poncelet, and M. Teisseire, "Incremental mining of sequential patterns in large databases," Data & Knowledge Engineering 46, pp. 97-121, 2003.
    [55] F. Gorunescu, Data Mining: Concepts, Models and Techniques: Springer-Verlag Berlin Heidelberg, 2011.
    [56] M. F. Award. Morningstar Fund Award - Taiwan Stock. Available: http://smart.businessweekly.com.tw/event/2012/fundawards/01_about_3.php
    [57] F. Reilly and K. Brown., "Investment Analysis and Portfolio Management, 7th," ed: South-Western Publishing, 2003.

    QR CODE