跳到主要內容

臺灣博碩士論文加值系統

(18.207.132.116) 您好!臺灣時間:2021/07/29 21:34
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:魏宛茹
研究生(外文):Wan-Ru Wei
論文名稱:資料前處理對於支援向量機之影響-以期貨預測為例
論文名稱(外文):The Influence of Data Preprocess on Support Vector Machines - Applications to Futures Forecasting
指導教授:白炳豐白炳豐引用關係
指導教授(外文):Ping-Feng Pai
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:61
中文關鍵詞:臺灣股價指數期貨資料前處理支援向量機
外文關鍵詞:Taiwan stock index futuresData preprocessSVM
相關次數:
  • 被引用被引用:2
  • 點閱點閱:160
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在變化快速的金融市場上,已有多種投資商品供投資者選擇。臺灣股價指數期貨是一種低成本高報酬的商品,若能有效預測期貨的變化趨勢,可幫助投資者以有限的資金成本獲取最大的投資報酬。支援向量機(Support Vector Machines)具有處理線性與非線性問題的能力,它使用結構風險最小誤差的概念,解決類神經網路過度學習的問題。
本研究結合資料前處理方法與支援向量機預測期貨的漲跌幅度,將決策屬性分為漲跌與5類區間來探討。首先將屬性資料正規化至[0;1]的範圍內,再利用主成份分析、判別分析與粗略集合論等篩選重要的屬性,支援向量機預測期貨的漲跌幅度,並將結果與倒傳遞網路、判別分析比較。結果顯示,利用資料前處理與支援向量機有最佳的表現,可提供使用者在投資期貨時參考。
In the variable financial markets, many commodities have been provided for investors. Taiwan stock index futures is a commodity of using finite bankroll to earn profits. If we can forecast the movements of futures prices, it helps investors earn enormous profits. Support Vector Machines (SVM) can handle linear and nonlinear problems. It is based on structural risk minimization principle to explore the minimization of an upper bound with forecasting error. It can avoid the problems of over-fitting and improve performance.
In this paper, we integrated data preprocess and SVM to forecast the price fluctuation of futures. We discussed price fluctuation and 5-classes interval of decision attribute. First, we normalized data to the range of [0;1]. Then, we used Principal Component Analysis (PCA), Discriminant Analysis (DA) and Rough Set (RS) to select important attributes and SVM to forecast volatility of Taiwan stock index futures. To evaluate the forecasting ability of SVM, we compared the performance with Backpropagation Neural Network (BP) and DA. The experiment results showed that the best performance integrated data preprocess and SVM to forecast futures can provide to investors.
目錄
第一章緒論1
1.1研究背景與動機1
1.2研究目的3
1.3研究範圍3
1.4論文架構4
第二章文獻探討5
2.1期貨5
2.1.1期貨交易理論5
2.1.2臺股指數期貨7
2.2台股指數期貨預測模式相關文獻探討9
2.3資料前處理之相關文獻探討11
2.4判別分析於預測相關文獻之探討15
2.5倒傳遞網路於預測相關文獻之探討16
2.6支援向量機於預測相關文獻之探討18
第三章研究方法20
3.1研究架構圖20
3.2影響台股指數期貨的變數21
3.3資料前處理23
3.3.1主成份分析24
3.3.2粗略集合論26
3.4預測模式方法27
3.4.1免疫演算法27
3.4.2判別分析29
3.4.3倒傳遞網路31
3.4.4支援向量機33
3.5結合免疫演算法與支援向量機37
第四章實證研究與分析40
4.1資料前處理41
4.1.1正規化41
4.1.2屬性篩選41
4.2預測模式42
4.3分5類漲跌區間43
4.3.1資料前處理43
4.3.2預測模型46
4.4漲跌47
4.4.1資料前處理47
4.4.2預測模型49
第五章結論與未來研究51
5.1結論與建議51
5.2後續研究52
參考文獻53
陳正昌、陳新豐、程炳林、劉子鍵(民94)。多變量析方法-統計軟體應用(四版),臺北市:五南。
葉怡成(民92)。類神經網路模式應用與實作(八版),臺北市:儒林。
臺灣期貨交易所,http://www.taifex.com.tw/chinese/home.htm。
Ahn, B. S., Cho, S.S. & Kim, C. Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18, 65–74.
Bergerson, K. & Wunsch, D. C. (1991). A Commodity Trading Model Based on a Neural Network-Expert System Hybrid. International Joint Conference on Neural Networks, I, 289-293, Seattle, 1991.
Chang, T. C. & Chao, R. J. (2006). Application of back-propagation networks in debris flow prediction. Engineering Geology, 85, 270–280.
Chen, A. P. & Chang, Y. H. (2005). Using Extended Classifier System to Forecast S&P Futures Based on Contrary Sentiment Indicators. The 2005 IEEE Congress on Evolutionary Computation, 3, 2084-2090. Edinburgh, UK, September 2005.
Chi, S. C., Chen, H. P. & Cheng, C. H. (1999). A forecasting approach for stock index future using grey theory and neural network. IEEE International Joint Conference on Neural Networks, 3850-3855. Washington D.C., 1999.
Clark, J., Koprinska, I. & Poon, J. (2003). A Neural Network Based Approach to Automated E-mail Classification. IEEE/WIC International Conference on Web Intelligence, 702-705, October 13-17, 2003.
Comaka, E., Arslana, A. & Turkoğlub, İ. (2007). A decision support system based on support vector machines for diagnosis of the heart valve diseases. Computers in Biology and Medicine, 37, 21 – 27.
Copsey, I.. (1999). Integrated technical analysis, Wiley.
Crone, S. F., Lessmann, S. & Stahlbock, R. (2006). The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing. European Journal of Operational Research, 173, 781–800.
Daia, D. Q. & Yuen, P. C. (2003). Regularized discriminant analysis and its application to face recognition. Pattern Recognition, 36, 845 – 847.
Dong, J. X., Krzyzak, A. & Suen, C. Y. (2005). An improved handwritten Chinese character recognition system using support vector machine. Pattern Recognition Letters, 26, 1849–1856.
Drucker, H., Wu, D. & Vapnik, V. N. (1999). Support Vector Machines for Spam Categorization. IEEE Transactions on Neural Networks, 10(5), 1048-1054.
Etzkorn, M. (1997). Trading with oscillators: pinpointing market extremes - theory and practice, John wiley
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188.
Fletcher, R. (1987). Practical methods of optimization(2nd). John Wiley and Sons, Inc.
Gabrielle, W. P., Dale, L. & Lekov, A. (2006). Comparing price forecast accuracy of natural gas models and futures markets. Energy Policy, 34, 4115-4122.
Gold, C., Holub, A. & Sollich, P. (2005). Bayesian approach to feature selection and parameter tuning for support vector machine classifiers. Neural Networks, 18, 693–701.
Grudnitski, G. & Osburn, L. (1993). Forecasting S&P and Gold Futures Prices: An Application of Neural Networks. Journal of Futures Markets, 13(6), 631-643.
Gunn, S. R. (1998). Support Vector machines for classification and regression. Technical Report, Department of Electronics and Computer Science, University of Southampton.
Hamid, S. A. & Iqbal, Z. (2004). Using neural network for forecasting volatility of S&P 500 index futures prices. Journal of Business Research, 57, 1116-1125.
Haykin, S. (1999), Neural networks: A comprehensive foundation. Upper Saddle River: Prentice-Hall.
Hong, W. C., Lai, F. M., Wu, J. H., Pai, P. F. & Yang, S. L. (2006). Feasibility assessment of support vector regression models with immune algorithms in predicting fatigue life of composites. Join Conference on Information Sciences, Taiwan, 1220-1223.
Hornik, K., Stinchcombe, M. & White, H. (1989). Multilayer feedforward networks are universal approximations. Neural Networks, 2, 336–359.
Hotelling, H. (1933). Analysis of a Complex of Statistical Variables into Principal Components. Journal of Education Psychology, 24, 498-520.
Hsu, C. C. & Chen, C. Y. (2003). Regional load forecasting in Taiwan–applications of artificial neural networks. Energy Conversion and Management, 44, 1941-1949.
Hsu, C. W. & Lin, C. J. (2002). A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks, 13(2), 415-425.
Huang, T. M. & Kecman, V. (2005). Gene extraction for cancer diagnosis by support vector machines-An improvement. Artificial Intelligence in Medicine, 35, 185-194.
Huang, W., Nakamori, Y. & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32, 2513-2522.
Hunt, J. E. & Cooke, D. E. (1996). Learning using an artificial immune system. Journal of Network and Computer Applications, 19, 189-212.
Ivancevic, V., Kaine, A. K., McLindin, B. A. & Sunde, J. (2003). Factor Analysis of Essential Facial Features. The 25th International Conference on Information Technology Interfaces, 16-19, Cavtat, Croatia, June 2003.
Jo, H. & Han, I. (1996). Integration of Case-Based Forecasting, Neural Network, and Discriminant Analysis for Bankruptcy Prediction. Expert System With Application, 11(4), 415-422.
Johnson, R. A. & Wichern, D.W. (1993). Applied multivariate statistical analysis. Upper Saddle River, NJ: Prentice-Hall.
Kaiser, H. F. (1958). The Varimax Criterion for Analytic Rotation in Factor Analysis. Psychometrika, 23, 187-200.
Khaw, J. F. C., Lim, B. S. & Lim, L. E. N. (1995). Optimal Design of Neural Networks Using the Taguchi Method. Neurocomputing, 7(3), 225-245.
Kim, J. C., Kim, D. H., Kim, J. J., Ye, J. S. & Lee, H. S. (2000), Segmenting the Korean housing market using multiple discriminant analysis. Construction Management and Economics, 18(1), 45-54.
Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55, 307 – 319.
Kim, K. I., Jung, K. & Kim, J. H. (2003). Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm. IEEE Transaction on Pattern Analysis and Machine Intelligence, 25(12), 1631-1639.
Lee, T. L. (2004). Back-propagation neural network for long-term tidal predictions. Ocean Engineering, 31, 225–238.
Lee, T. S. & Chen, I. F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications , 28, 743–752.
Lee, T. S., Chiu, C. C., Lu, C. J. & Chen, I. F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 23, 245-254.
Lee, Y. C. (2007). Application of support vector machines to corporate credit rating prediction. Expert Systems with Applications, 33, 67–74.
Li, M. & Yuan, B. (2005). 2D-LDA: A statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, 26, 527–532.
Li, Q., Jiao, L. & Hao, Y. (2007). Adaptive simplification of solution for support vector machine. Pattern Recognition, 40, 972-980.
Li, R. & Wang, Z. O. (2004). Mining classification rules using rough sets and neural networks. European Journal of Operational Research, 157, 439-448.
Li, Y., Cai, Y. Z., Li, Y. C. & Xu, X. M. (2004). Rough Sets Method for SVM Data Preprocessing. IEEE Conference on Cybernetics and intelligent Systems, 2, 1039-1042, Singapore, 2004
Lin, C. H., Chen, C. S., Wu, C. J. & Kang, M. S. (2003). Application of immune algorithm to optimal switching operation for distribution-loss minimization and loading balance. IEE Proceedings, Generation, Transmission, and Distribution, 150(2), 183-189.
Lin, H. T. & Lin, C. J. (2003). A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Mar, 2003.
Liu, Y. and Zheng, Y. F. (2006). FS_SFS: Anovel feature selection method for support vector machines. Pattern Recognition, 39, 1333 – 1345.
Luh, G. C. & Chueh, C. H. (2004). Multi-modal topological optimization of structure using immune algorithm. Computer methods in applied mechanics and engineering, 193, 4035–4055.
Martín Y. G., Pavόn, J. L. P., Cordero, B. M. & Pinto, C. G.. (1999). Classification of vegetable oils by linear discriminant analysis of Electronic Nose data. Analytica Chimica Acta, 384, 83-94.
Marusic, A. (2000). Factor analysis of risk for coronary heart disease: an independent replication. International Journal of Cardiology, 75(2), 233–238.
McKee, T. E. & Lensberg, T. (2002). Genetic programming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research, 138, 436-451.
Nutman, N., Solomon, Y., Mendel, S., Nutman, J., Hines, E., Topilsky, M. & Kivity, S. (1998). The use of a neural network for studying the relationship between air pollution and asthma-related emergency room visits. Respirtory Medicine, 92(10), 1199-1202.
Pai, P. F. (2006). System reliability forecasting by support vector machines with genetic algorithms. Mathematical and Computer Modelling, 43, 262–274.
Pai, P. F. & Hong, W. C. (2005). Support Vector Machines with Simulated Annealing Algorithm in Electricity Load Forecasting. Energy Conversion and Management, 46, 2669-2688.
Pai, P. F. & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33, 497-505.
Pawlak, Z. (1982). Rough sets. International Journal of Information and Computer Sciences, 11(1), 341-356.
Platt, J. C., Cristianini, N. & Taylor, J. S. (2000). Large DAGs for multiclass classification. In Advance in Neural Information Processing Systems, 12, 547–553, MIT Press.
Sadorsky, P. (2006). Modeling and forecasting petroleum futures volatility. Energy Economics, 28, 467-488.
Sahoo, G. B. & Ray, C. (2006). Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms. Journal of Membrane Science, 283, 147–157.
Shen, L., Tay, F. E. H., Qu, L. & Shen, Y. (2000). Fault diagnosis using Rough Sets Theory. Computers in Industry, 43, 61-72.
Shin, K. S., Lee, T. S. & Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert System with Application, 28, 127-135.
Stepaniuk, J. & Kierzkowska, K. (2003). Hybrid Classifier Based on Rough Sets and Neural Networks. Electronic Notes in Theoretical Computer Science, 82, 1-11.
Sung, T. K., Chang, N. & Lee, G. (1999). Dynamics of modeling in data mining; interpretive approach to bankruptcy prediction. Journal of Management Information Systems, 16(1), 63-85.
Swiniarski, R. W. & Hargis, L. (2001). Rough sets as a front end of neural-networks texture classifiers. Neurocomputing, 36, 85-102.
Swiniarski, R. W. & Skowron, A. (2003). Rough set methods in feature selection and recognition. Pattern Recognition Letters, 24, 833–849.
Trappey, A. J. C., Hsu, F. C., Trappey, C. V. & Lin, C. I. (2006). Development of a patent document classification and search platform using a back-propagation network. Expert Systems with Applications, 31, 755–765.

Tsaih, R., Hsu, Y. & Lai, C. C. (1998). Forecasting S&P 500 stock index fetures with a hybrid AI system. Decision Support System, 23, 161-174.
Vapnik, V.N. (1995). The nature of statistical learning theory. New York: Springer.
Vellido, A., Lisboa, P. J. G. & Vaughan, J. (1999). Neural networks in business: a survey of applications. Expert Systems With Applications, 17, 51–70.
Wang, X., Yang, J., Teng, X., Xia, W. & Jensen, R. (2007). Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters, 28, 459–471.
Werbos, P. J. (1974). Beyond Regression: New tools for Prediction and Analysis in the Behavioral Sciences. Harvard University.
Wong, F.S. (1991). Time series forecasting using backpropagation neural networks. Neurocomputing, 2, 147–159.
Wong, W. T. & Hsu, S. H. (2006). Application of SVM and ANN for image retrieval. European Journal of Operational Research, 173, 938–950.
Worth, A. P. and Cronin, M. T. D. (2003). The use of discriminant analysis, logistic regression and classification tree analysis in the development of classification models for human health effects. Journal of Molecular Structure (Theochem), 622, 97–111.
Yang, H. H., Liu, T. C. & Lin, Y. T. (2007). Applying rough sets to prevent customer complaints for IC packaging foundry. Expert Systems With Application, 32, 151-156.
Yao, J., Li, Y. & Tan, C. L. (2000). Option price forecasting using neural networks. Omega, 28. 455-466.
Yim, J. & Mitchell, H. (2005). Comparison of country risk models: hybrid neural networks, logit models, discriminant analysis and cluster techniques. Expert Systems with Applications, 28, 137–148.
Zhou, Y., Hahn, J. & Mannan, M. S. (2006). Process monitoring based on classification tree and discriminant analysis. Reliability Engineering and System Safety, 91, 546–555.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
1. 張淑美(1994)。不同地區教育機會差異之探討。高雄師大學報,5:87-111。
2. 張淑美(1994)。不同地區教育機會差異之探討。高雄師大學報,5:87-111。
3. 張淑美(1994)。不同地區教育機會差異之探討。高雄師大學報,5:87-111。
4. 夏曉鵑(1997)。女性身體的貿易----台灣印尼新娘貿易的階級、族群關係與性別分析。騷動,4,10- 21。
5. 夏曉鵑(1997)。女性身體的貿易----台灣印尼新娘貿易的階級、族群關係與性別分析。騷動,4,10- 21。
6. 夏曉鵑(1997)。女性身體的貿易----台灣印尼新娘貿易的階級、族群關係與性別分析。騷動,4,10- 21。
7. 吳武典 (民86)。國中偏差行為學生學校生活適應之探討。教育心理學報,29期,25-50頁。
8. 吳武典 (民86)。國中偏差行為學生學校生活適應之探討。教育心理學報,29期,25-50頁。
9. 吳武典 (民86)。國中偏差行為學生學校生活適應之探討。教育心理學報,29期,25-50頁。
10. 王天佑(2001)。臺灣原漢族群社會流動之差異與變遷。社會文化學報,13:39-70。
11. 王天佑(2001)。臺灣原漢族群社會流動之差異與變遷。社會文化學報,13:39-70。
12. 王天佑(2001)。臺灣原漢族群社會流動之差異與變遷。社會文化學報,13:39-70。
13. 陳源湖(2003)。外籍新娘識字教育之探析。載愉教育部社教司舉辦之「九十二年全國外籍新娘成人教育研討會」手冊(75-85),台北。
14. 陳源湖(2003)。外籍新娘識字教育之探析。載愉教育部社教司舉辦之「九十二年全國外籍新娘成人教育研討會」手冊(75-85),台北。
15. 陳源湖(2003)。外籍新娘識字教育之探析。載愉教育部社教司舉辦之「九十二年全國外籍新娘成人教育研討會」手冊(75-85),台北。