臺灣博碩士論文加值系統

由於台灣目前的經濟體系和薪資水平都不盡理想，投資理財成為許多人用來增加額外收入的主要管道。股票市場是一個波動頻繁且複雜的環境，股票價格更是極度非線性，因此每位投資者都希望能夠藉由評估工具來降低投資的風險。本研究使用學者所提出的SWI-GSP演算法作為基礎進行改進，提出第三種模式為上漲對應漲跌與下跌對應漲跌之混合模式，並將此模式加入演算法，對演算法進行調整，欲找出A公司股價上下波動，經過了N個天數或周期後B公司股價漲跌的模式。研究結果在上漲、下跌和混合序列模式中，皆為宏達電與聯發科之關聯支持度最高，本研究所提出之混合序列模式也增加了規則的參考依據，將觀點拓展至四個層面，不論對於已經持有股票或是尚未購買股票的投資人皆有幫助，在實驗結果也表明SWII-GSP演算法所考量的層面是需要存在的。

Because Taiwans current economic system and salary levels are not ideal, investment and wealth management has become the main conduit for many people to increase their income. The stock market is a highly volatile and complex environment, and stock prices are extremely non-linear, so every investor wants to reduce the risk of investment by using evaluation tools. In this study, the SWI-GSP algorithm proposed by scholars is used as the basis for improvement. The third model is proposed to increase the mixed pattern of rising and falling corresponding to the rise and fall, and add this model to the algorithm to adjust the algorithm. In order to find out the fluctuation of the companys share price, after the N days or cycles, the companys share price rose and fell. The results of the research in the rising, falling and mixed sequence models are the highest degree of support for HTC and MediaTek. The hybrid sequence model proposed by this study also increases the reference basis of the rules and extends the viewpoint to four levels, regardless of Investors who hold stocks or have not yet purchased stocks are helpful, and the experimental results also indicate that the level of consideration of the SWII-GSP algorithm needs to exist.

摘要 i
ABSTRACT ii
目錄 iii
表目錄 iv
圖目錄 v
1. 緒論 1
2. 文獻探討 3
2.1 股票預測智能方法 3
2.2 序列模式探勘 4
2.2.1 AprioriAll 7
2.2.2 Generalized Sequential Pattern 10
3. 研究方法 12
3.1研究情境 12
3.2研究架構與流程 12
3.3 研究提出之演算法 13
3.3.1 SWI-GSP演算法 13
3.3.2 SWII-GSP演算法 17
4. 實驗設計 24
4.1資料來源 24
4.2實驗設計流程 25
5. 實驗結果 27
5.1上漲序列模式 28
5.2下跌序列模式 30
5.3混合序列模式 32
5.4結果討論 35
6. 結論與未來研究 36
6.1結論 36
6.2未來研究 37
參考文獻 38

英文文獻
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Acm sigmod record (Vol. 22, No. 2, pp. 207-216). ACM.
Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487-499).
Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In icde (Vol. 95, pp. 3-14).
Ahmad, K., Oliveira, P. C. F. D., Manomaisupat, P., Casey, M. & Taskaya, T.(2002). Description of events: An analysis of keywords and indexical names.Proceedings of the third international conference on language resources andevaluation, LREC 2002: Workshop on event modelling for multilingual documentlinking, 29-35.
Avcı, E. (2007). Forecasting daily and sessional returns of the ISE-100 index with neural network models.
Argiddi, R. V., & Apte, S. (2012). Future trend prediction of indian it stock market using association rule mining of transaction data. International Journal of Computer Applications, 39(10).
Arafah, A. A., & Mukhlash, I. (2015). The application of fuzzy association rule on co-movement analyze of Indonesian stock price. Procedia Computer Science, 59, 235-243.
Asadifar, S., & Kahani, M. (2017). Semantic association rule mining: a new approach for stock market prediction. In Swarm Intelligence and Evolutionary Computation (CSIEC), 2017 2nd Conference on (pp. 106-111). IEEE.
Anwar, T., & Uma, V. (2019). CD-SPM: Cross-domain book recommendation using sequential pattern mining and rule mining. Journal of King Saud University-Computer and Information Sciences.
Chen, T. L., Cheng, C. H., & Teoh, H. J. (2007). Fuzzy time-series based on Fibonacci sequence for stock price forecasting. Physica A: Statistical Mechanics and its Applications, 380, 377-390.markets. Unpublished working paper. Duke University, Durham, North Carolina.
Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340-355.
Ding, X., Zhang, Y., Liu, T., & Duan, J. (2015). Deep learning for event-driven stock prediction. In Twenty-Fourth International Joint Conference on Artificial Intelligence.
Egeli, B., Ozturan, M., & Badur, B. (2003). Stock market prediction using artificial neural networks. In Proceedings of the 3rd Hawaii International Conference on Business (pp. 1-8).
Fan, Y., Ye, Y., & Chen, L. (2016). Malicious sequential pattern mining for automatic malware detection. Expert Systems with Applications, 52, 16-25
Gong, X., Si, Y. W., Fong, S., & Biuk-Aghai, R. P. (2016). Financial time series pattern matching with extended UCR Suite and Support Vector Machine. Expert Systems with Applications, 55, 284-296.
Hipp, J., Güntzer, U., & Nakhaeizadeh, G. (2000). Algorithms for association rule mining—a general survey and comparison. ACM sigkdd explorations newsletter, 2(1), 58-64.
Han, J. and Kamber, M. 2000. Data Mining Concepts and Techniques. Morgan Kanufmann.
Hu, Z., Zhu, J., & Tse, K. (2013). Stocks market prediction using support vector machine. In Information Management, Innovation Management and Industrial Engineering (ICIII), 2013 6th International Conference on (Vol. 2, pp. 115-118). IEEE.
Hu, Y., Feng, B., Zhang, X., Ngai, E. W. T., & Liu, M. (2015). Stock trading rule discovery with an evolutionary trend following model. Expert Systems with Applications, 42(1), 212-222.
Hsu, M. W., Lessmann, S., Sung, M. C., Ma, T., & Johnson, J. E. (2016). Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert Systems with Applications, 61, 215-234.
Hommes, C. (2017). Booms, busts and behavioural heterogeneity in stock prices. Journal of Economic Dynamics and Control, 80, 101-124.
Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990). Stock market prediction system with modular neural networks. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 1-6). IEEE.
Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1), 307-319.
KARAATLI, M., GÜNGÖR, İ., DEMİR, Y., & KALAYCI, Ş. (2005). Estimating stock market movements with neural network approach. Yönetim ve Ekonomi Araştırmaları Dergisi; Sayı: 3; 38-48.
Kazem, A., Sharifi, E., Hussain, F. K., Saberi, M., & Hussain, O. K. (2013). Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied soft computing, 13(2), 947-958.
Li, H., Sun, J., & Sun, B. L. (2009). Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors. Expert Systems with Applications, 36(1), 643-659.
Luo, L., & Chen, X. (2013). Integrating piecewise linear representation and weighted support vector machine for stock trading signal prediction. Applied Soft Computing, 13(2), 806-816.
Li, H., Zhou, X., & Pan, C. (2015). Study on GSP algorithm based on Hadoop. In 2015 IEEE 5th International Conference on Electronics Information and Emergency Communication (pp. 321-324). IEEE.
Liu, H., & Du, H. (2017). Stock Sequence Pattern Mining Method Based on SWI-GSP Algorithm. In Proceedings of the 2017 International Conference on Data Mining, Communications and Information Technology (p. 3). ACM.
Le, H. H., Edman, H., Honda, Y., Kushima, M., Yamazaki, T., Araki, K., & Yokota, H. (2017). Fast Generation of Clinical Pathways including Time Intervals in Sequential Pattern Mining on Electronic Medical Record Systems. In 2017 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1726-1731). IEEE.
Miao, J., Wang, P., & Xu, Z. (2015). A Bayesian dynamic stochastic general equilibrium model of stock market bubbles and business cycles. Quantitative Economics, 6(3), 599-635.
Mattioli, G., Anable, J., & Vrotsou, K. (2016). Car dependent practices: Findings from a sequence pattern mining study of UK time use data. Transportation Research Part A: Policy and Practice, 89, 56-72.
Nair, B. B., Mohandas, V. P., & Sakthivel, N. R. (2010). A decision tree—rough set hybrid system for stock market trend prediction. International Journal of Computer Applications, 6(9), 1-6.
Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41(16), 7653-7670.
Niyazmand, T., & Izadi, I. (2019). Pattern mining in alarm flood sequences using a modified PrefixSpan algorithm. ISA transactions.
Olson, D., & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453-465.
Paranjape-Voditel, P., & Deshpande, U. (2011). An association rule mining based stock market recommender system. In Emerging Applications of Information Technology (EAIT), 2011 Second International Conference on (pp. 21-24). IEEE.
Paranjape-Voditel, P., & Deshpande, U. (2013). A stock market portfolio recommender system based on association rule mining. Applied Soft Computing, 13(2), 1055-1063.
Rounaghi, M. M., & Zadeh, F. N. (2016). Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model. Physica A: Statistical Mechanics and its Applications, 456, 10-21.
Srikant, R., & Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. In International Conference on Extending Database Technology (pp. 1-17). Springer, Berlin, Heidelberg.
Sorensen, E. H., Miller, K. L., & Ooi, C. K. (2000). The decision tree approach to stock selection. The Journal of Portfolio Management, 27(1), 42-52.
Shaochum Wu ; Minfu Fang ; Yinyin Li ; Bofeng Zhang.(2008). An Earthquake Sequential Pattern Mining Algorithm Based on General Constraint. 2008 The 9th International Conference for Young Computer Scientists. 1878 - 1883
Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 12.
Thirunavukarasu, P. (2009). Estimation of return on investment in share market through ann. Global Journal of Finance and Management, 1(2), 113-122.
Teixeira, L. A., & De Oliveira, A. L. I. (2010). A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert systems with applications, 37(10), 6885-6890.
Tsai, C. Y., & Lai, B. H. (2015). A location-item-time sequential pattern mining algorithm for route recommendation. Knowledge-Based Systems, 73, 97-110.
Van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449-472.
White, H. (1988). Economic prediction using neural networks: The case of IBM daily stock returns.
Wang, X. Y., & Wang, Z. O. (2002). Stock market time series data mining based on regularized neural network and rough set. In Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on (Vol. 1, pp. 315-318). IEEE.
Walter Sun(2003) . Relationship between Trading Volume and Security Prices and Returns . MIT Laboratory for Information and Decision Systems Technical Report P-2638
Wu, M. C., Lin, S. Y., & Lin, C. H. (2006). An effective application of decision tree to stock trading. Expert Systems with Applications, 31(2), 270-274.
Wright, A. P., Wright, A. T., McCoy, A. B., & Sittig, D. F. (2015). The use of sequential pattern mining to predict next prescribed medications. Journal of biomedical informatics, 53, 73-80.
Wang, L., Wang, Z., Zhao, S., & Tan, S. (2015). Stock market trend prediction using dynamical Bayesian factor graph. Expert Systems with Applications, 42(15), 6267-6275.
Xi, L., Muzhou, H., Lee, M. H., Li, J., Wei, D., Hai, H., & Wu, Y. (2014). A new constructive neural network method for noise processing and its application on stock market prediction. Applied Soft Computing, 15, 57-66.
Xi, Y., Peng, H., Qin, Y., Xie, W., & Chen, X. (2015). Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets. Mathematics and Computers in Simulation, 117, 141-153.
Yoon, Y., & Swales, G. (1991). Predicting stock price performance: A neural network approach. In System Sciences, 1991. Proceedings of the Twenty-Fourth Annual Hawaii International Conference on (Vol. 4, pp. 156-162). IEEE.
Yang, H., Chan, L., & King, I. (2002). Support vector machine regression for volatile stock market prediction. Intelligent Data Engineering and Automated Learning—IDEAL 2002, 143-152.
Yu, H., Chen, R., & Zhang, G. (2014). A SVM stock selection model within PCA. Procedia computer science, 31, 406-412.
Yuan, X., Chang, W., Zhou, S., & Cheng, Y. (2018). Sequential Pattern Mining Algorithm Based on Text Data: Taking the Fault Text Records as an Example. Sustainability, 10(11), 4330.
Zhang, M., Kao, B., Yip, C. L., & Cheung, D. (2001). A GSP-based efficient algorithm for mining frequent sequences. In Proc. of IC-AI (pp. 497-503).
Zhao, Q., & Bhowmick, S. S. (2003). Sequential pattern mining: A survey. ITechnical Report CAIS Nayang Technological University Singapore, 1, 26.
Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126-139.

中文文獻
邱雅鈴.(2004). 基因演算法於股價走勢上之應用—以台積電為例. 國立高雄第一科技大學-風險管理與保險研究所碩士論文.
陳振東, 張羽欣, 蘇子瑋, & 詹茗淇. (2017). 時間序列法與類神經網路於股票漲跌預測比較之研究. 管理資訊計算, 6, 136-146.