Building a Trend Based Segmentation Method with SVR Model for Stock Turning Detection
This research focus on developing a new segmentation method for improving forecasting model which is call trend based segmentation method (TBSM). Generally, the piece-wise linear representation (PLR) can finds some of pair of trading points is well for time series data, but in the complicated stock environment it is not well for stock forecasting because of the stock has more trends of trading. If we consider the trends of trading in stock price for the trading signal which it will improve the precision of forecasting model. Therefore, a TBSM with SVR model used to detect the trading points for various stocks of Taiwanese and America under different trend tendencies. The experimental results show our trading system is more profitable and can be implemented in real time of stock market
[1] P.C. Chang, C.Y. Tsai, C.H. Huang, and C.Y. Fan, "Application of a case
base reasoning based support vector machine for financial time series
data forecasting," in International Conference on Intelligent Computing,
South Korea, 2009, pp. 294-304.
[2] P.C. Chang, C.Y. Fan, and C.H. Liu, "Integrating a piecewise linear
representation method and a neural network model for stock trading
points prediction,´ IEEE Transactions on Systems, Man, and Cybernetics,
Part C: Applications and Reviews, vol. 39, 2009, pp. 80-92.
[3] X.P. Ge, "Pattern Matching in Financial Time Series Data, "Computer
Communications, 27, 1998, pp. 935-945.
[4] E. Keogh and M. Pazzani, "An enhanced representation of time series
which allows fast and accurate classification," clustering and relevance
feedback. In: KDD, 2009, pp. 239-243.
[5] V. Lavrenko, M. Schmill, D. Lawrie, P.Ogilvie, D. Jensen, and J. Allan,
"Mining of concurrent text and time series, " In: Proceedings of the 6th
International Conference on Knowledge Discovery and Data Mining,
2000, pp. 37-44.
[6] Alex J. Smola and Bernhard Schölkopf, "A tutorial on support vector
regression. Statistics and Computing," vol. 14, 2004, PP. 199-222.
[7] Depei Baoi and Zehong Yang, "Intelligent stock trading system by
turning point confirming and probabilistic reasoning," Expert System
with Applications, vol. 34, 2008, pp. 620-627.
[8] Allan Timmermann and Clive W. J. Granger, "Efficient market
hypothesis and forecasting," International Journal of Forecasting, vol. 20,
2004, pp. 15-27.
[9] Y.W. Wang, P.C. Chang, C.Y. Fan, and C.H. Huang, "Database
classification by integrating a case-based reasoning and support vector
machine for induction,´ Journal of Circuits, Systems, and Computers,
vol. 19, no. 1, 2010, pp. 31-44.
[10] Li Zhang, W. D. Zhou, and P.C. Chang, "Generalized nonlinear
discriminant analysis and its small sample size problems,"
Neurocomputing, vol. 74, no.4, 2011, pp. 568-574.
[11] V. Vapnik, "The nature of statistical learning theory, " Springer, 1995.
[12] Nicholas I. Sapankevych and Ravi Sankar, " Time series prediction using
support vector machines: a survey," IEEE Computational Intelligence
Magazine, vol. 4, 2009, pp. 24-38.
[13] J.L. Wu, L.C. Yu, and P.C. Chang, Emotion classification by removal of
the overlap from incremental association language features, "Journal of
the Chinese Institute of Engineers 34(7), pp. 947-955, October, 2011.
[14] Y. Zhu, D. Wu, S. Li, " A piecewise linear representation method of time
series based on feature pints," The proceeding of KES 2007/ WIRN 2007,
Part II, Vietri sul Mare, Italy, pp. 1066- 1072, January , 2007.
[15] Huanmei Wu, Betty Salzberg, and Donghui Zhang, " Online event-driven
subsequence matching over financial data streams," The proceedings of
the 2004 ACM SIGMOD, 2004, pp.23-34.
[16] Z. Zhang and J. Siekmann "Pattern Recognition in Stock Data Based on
a New Segmentation Algorithm, "Springer, 4798, 2007, pp. 520-525.
[17] J. Lachaud, A. Vialard, F. de Vieilleville, "Analysis and comparative
evaluation of discrete tangent estimators,´ In: Andres, E., Damiand, G.,
Lienhardt, P. (eds) DGCI 2005. LNCS, Springer, vol. 3429, 2005, pp.
240-251.
[18] Thomas J. Burkholder and Richard L. Lieber, "Stepwise regression is an
alternative to splines for fitting noisy data,´ Journal of Biomechanics, vol.
29, no. 2, 1996, pp.235-238.
[1] P.C. Chang, C.Y. Tsai, C.H. Huang, and C.Y. Fan, "Application of a case
base reasoning based support vector machine for financial time series
data forecasting," in International Conference on Intelligent Computing,
South Korea, 2009, pp. 294-304.
[2] P.C. Chang, C.Y. Fan, and C.H. Liu, "Integrating a piecewise linear
representation method and a neural network model for stock trading
points prediction,´ IEEE Transactions on Systems, Man, and Cybernetics,
Part C: Applications and Reviews, vol. 39, 2009, pp. 80-92.
[3] X.P. Ge, "Pattern Matching in Financial Time Series Data, "Computer
Communications, 27, 1998, pp. 935-945.
[4] E. Keogh and M. Pazzani, "An enhanced representation of time series
which allows fast and accurate classification," clustering and relevance
feedback. In: KDD, 2009, pp. 239-243.
[5] V. Lavrenko, M. Schmill, D. Lawrie, P.Ogilvie, D. Jensen, and J. Allan,
"Mining of concurrent text and time series, " In: Proceedings of the 6th
International Conference on Knowledge Discovery and Data Mining,
2000, pp. 37-44.
[6] Alex J. Smola and Bernhard Schölkopf, "A tutorial on support vector
regression. Statistics and Computing," vol. 14, 2004, PP. 199-222.
[7] Depei Baoi and Zehong Yang, "Intelligent stock trading system by
turning point confirming and probabilistic reasoning," Expert System
with Applications, vol. 34, 2008, pp. 620-627.
[8] Allan Timmermann and Clive W. J. Granger, "Efficient market
hypothesis and forecasting," International Journal of Forecasting, vol. 20,
2004, pp. 15-27.
[9] Y.W. Wang, P.C. Chang, C.Y. Fan, and C.H. Huang, "Database
classification by integrating a case-based reasoning and support vector
machine for induction,´ Journal of Circuits, Systems, and Computers,
vol. 19, no. 1, 2010, pp. 31-44.
[10] Li Zhang, W. D. Zhou, and P.C. Chang, "Generalized nonlinear
discriminant analysis and its small sample size problems,"
Neurocomputing, vol. 74, no.4, 2011, pp. 568-574.
[11] V. Vapnik, "The nature of statistical learning theory, " Springer, 1995.
[12] Nicholas I. Sapankevych and Ravi Sankar, " Time series prediction using
support vector machines: a survey," IEEE Computational Intelligence
Magazine, vol. 4, 2009, pp. 24-38.
[13] J.L. Wu, L.C. Yu, and P.C. Chang, Emotion classification by removal of
the overlap from incremental association language features, "Journal of
the Chinese Institute of Engineers 34(7), pp. 947-955, October, 2011.
[14] Y. Zhu, D. Wu, S. Li, " A piecewise linear representation method of time
series based on feature pints," The proceeding of KES 2007/ WIRN 2007,
Part II, Vietri sul Mare, Italy, pp. 1066- 1072, January , 2007.
[15] Huanmei Wu, Betty Salzberg, and Donghui Zhang, " Online event-driven
subsequence matching over financial data streams," The proceedings of
the 2004 ACM SIGMOD, 2004, pp.23-34.
[16] Z. Zhang and J. Siekmann "Pattern Recognition in Stock Data Based on
a New Segmentation Algorithm, "Springer, 4798, 2007, pp. 520-525.
[17] J. Lachaud, A. Vialard, F. de Vieilleville, "Analysis and comparative
evaluation of discrete tangent estimators,´ In: Andres, E., Damiand, G.,
Lienhardt, P. (eds) DGCI 2005. LNCS, Springer, vol. 3429, 2005, pp.
240-251.
[18] Thomas J. Burkholder and Richard L. Lieber, "Stepwise regression is an
alternative to splines for fitting noisy data,´ Journal of Biomechanics, vol.
29, no. 2, 1996, pp.235-238.
@article{"International Journal of Information, Control and Computer Sciences:53333", author = "Jheng-Long Wu and Pei-Chann Chang and Yi-Fang Pan", title = "Building a Trend Based Segmentation Method with SVR Model for Stock Turning Detection", abstract = "This research focus on developing a new segmentation method for improving forecasting model which is call trend based segmentation method (TBSM). Generally, the piece-wise linear representation (PLR) can finds some of pair of trading points is well for time series data, but in the complicated stock environment it is not well for stock forecasting because of the stock has more trends of trading. If we consider the trends of trading in stock price for the trading signal which it will improve the precision of forecasting model. Therefore, a TBSM with SVR model used to detect the trading points for various stocks of Taiwanese and America under different trend tendencies. The experimental results show our trading system is more profitable and can be implemented in real time of stock market
", keywords = "Trend based segmentation method, support vector
machine, turning detection, stock forecasting.", volume = "6", number = "5", pages = "601-5", }
