- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 615152, 20 pages
A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting
Department of Information Management, Yuan Ze University, Taoyuan 32026, Taiwan
Received 10 February 2012; Accepted 28 March 2012
Academic Editor: Ming Li
Copyright © 2012 Jheng-Long Wu and Pei-Chann Chang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
- V. N. Vapnik, Statistical Learning Theory, Adaptive and Learning Systems for Signal Processing, Communications, and Control, John Wiley & Sons, New York, NY, USA, 1998.
- Z. Liu, “Chaotic time series analysis,” Mathematical Problems in Engineering, vol. 2010, Article ID 720190, 31 pages, 2010.
- B. J. Chen, M. W. Chang, and C. J. Lin, “Load forecasting using support vector machines: a study on EUNITE Competition 2001,” IEEE Transactions on Power Systems, vol. 19, no. 4, pp. 1821–1830, 2004.
- F. Girosi, M. Jones, and T. Poggio, “Regularization theory and neural networks architectures,” Neural Computation, vol. 7, pp. 219–269, 1995.
- X. H. Yang, D. X. She, Z. F. Yang, Q. H. Tang, and J. Q. Li, “Chaotic bayesian method based on multiple criteria decision making (MCDM) for forecasting nonlinear hydrological time series,” International Journal of Nonlinear Sciences and Numerical Simulation, vol. 10, no. 11-12, pp. 1595–1610, 2009.
- D. She and X. Yang, “A new adaptive local linear prediction method and its application in hydrological time series,” Mathematical Problems in Engineering, vol. 2010, Article ID 205438, 15 pages, 2010.
- N. Muttil and K. W. Chau, “Neural network and genetic programming for modelling coastal algal blooms,” International Journal of Environment and Pollution, vol. 28, no. 3-4, pp. 223–238, 2006.
- D. Niu, Y. Wang, and D. D. Wu, “Power load forecasting using support vector machine and ant colony optimization,” Expert Systems with Applications, vol. 37, no. 3, pp. 2531–2539, 2010.
- P. F. Pai and W. C. Hong, “Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms,” Electric Power Systems Research, vol. 74, no. 3, pp. 417–425, 2005.
- W. C. Hong, “Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model,” Energy Conversion and Management, vol. 50, no. 1, pp. 105–117, 2009.
- T. Farooq, A. Guergachi, and S. Krishnan, “Knowledge-based Green's Kernel for support vector regression,” Mathematical Problems in Engineering, vol. 2010, Article ID 378652, 16 pages, 2010.
- S. O. Lozza, E. Angelelli, and A. Bianchi, “Financial applications of bivariate Markov processes,” Mathematical Problems in Engineering, vol. 2011, Article ID 347604, 15 pages, 2011.
- A. Swishchuk and R. Manca, “Modeling and pricing of variance and volatility swaps for local semi-markov volatilities in financial engineering,” Mathematical Problems in Engineering, vol. 2010, Article ID 537571, 17 pages, 2010.
- M. S. Abd-Elouahab, N. E. Hamri, and J. Wang, “Chaos control of a fractional-order financial system,” Mathematical Problems in Engineering, vol. 2010, Article ID 270646, 18 pages, 2010.
- A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004.
- P. C. Chang and C. H. Liu, “A TSK type fuzzy rule based system for stock price prediction,” Expert Systems with Applications, vol. 34, no. 1, pp. 135–144, 2008.
- P. F. Pai and C. S. Lin, “A hybrid ARIMA and support vector machines model in stock price forecasting,” Omega, vol. 33, no. 6, pp. 497–505, 2005.
- F. X. Diebold and R. S. Mariano, “Comparing predictive accuracy,” Journal of Business and Economic Statistics, vol. 20, no. 1, pp. 134–144, 2002.
- H. Liu and J. Wang, “Integrating independent component analysis and principal component analysis with neural network to predict Chinese stock market,” Mathematical Problems in Engineering, vol. 2011, Article ID 382659, 15 pages, 2011.
- X. P. Ge, “Pattern matching in financial time series data,” Computer Communications, vol. 27, pp. 935–945, 1998.
- E. Keogh and M. Pazzani, “An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback,” in Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD '98), pp. 239–241, August 1998.
- 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 (KDD '00), pp. 37–44, August 2000.
- S. Ghosh, P. Manimaran, and P. K. Panigrahi, “Characterizing multi-scale self-similar behavior and non-statistical properties of fluctuations in financial time series,” Physica A, vol. 390, no. 23-24, pp. 4304–4316, 2011.
- 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 Proceedings of the International Conference on Intelligent Computing (ICIC '09), vol. 5755, pp. 294–304, September 2009.
- 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, vol. 39, no. 1, pp. 80–92, 2009.
- L. Todorova and B. Vogt, “Power law distribution in high frequency financial data? An econometric analysis,” Physica A, vol. 390, no. 23-24, pp. 4433–4444, 2011.
- M. K. P. So, C. W. S. Chen, J. Y. Lee, and Y. P. Chang, “An empirical evaluation of fat-tailed distributions in modeling financial time series,” Mathematics and Computers in Simulation, vol. 77, no. 1, pp. 96–108, 2008.
- M. Li and W. Zhao, “Visiting power laws in cyber-physical networking systems,” Mathematical Problems in Engineering, vol. 2012, Article ID 302786, 13 pages, 2012.
- L. Muchnik, A. Bunde, and S. Havlin, “Long term memory in extreme returns of financial time series,” Physica A, vol. 388, no. 19, pp. 4145–4150, 2009.
- M. Li, C. Cattani, and S. Y. Chen, “Viewing sea level by a one-dimensional random function with long memory,” Mathematical Problems in Engineering, vol. 2011, Article ID 654284, 13 pages, 2011.
- M. Li, “Fractal time series—a tutorial review,” Mathematical Problems in Engineering, vol. 2010, Article ID 157264, 26 pages, 2010.
- J. O. Lachaud, A. Vialard, and F. De Vieilleville, “Analysis and comparative evaluation of discrete tangent estimators,” in Proceedings of the 12th International Conference on Discrete Geometry for Computer Imagery (DGCI '05), E. Andres, G. Damiand, and P. Lienhardt, Eds., vol. 3429,, pp. 240–251, Springer, April 2005.
- Y. Zhu, D. Wu, and S. Li, “A piecewise linear representation method of time series based on feature pints,” in Proceedings of the11th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES '07), 17th Italian Workshop on Neural Networks (WIRN '07), pp. 1066–1072, January 2007.
- H. Wu, B. Salzberg, and D. Zhang, “Online event-driven subsequence matching over financial data streams,” in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD '04), pp. 23–34, June 2004.
- Z. Zhang, J. Jiang, X. Liu et al., “Pattern recognition in stock data based on a new segmentation algorithm,” in Proceedings of the 2nd International Conference on Knowledge Science, Engineering and Management (KSEM '07), vol. 4798 of Lecture Notes in Computer Science, pp. 520–525, 2007.
- 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, pp. 31–44, 2010.
- L. Zhang, W. D. Zhou, and P. C. Chang, “Generalized nonlinear discriminant analysis and its small sample size problems,” Neurocomputing, vol. 74, no. 4, pp. 568–574, 2011.
- N. Sapankevych and R. Sankar, “Time series prediction using support vector machines: a survey,” IEEE Computational Intelligence Magazine, vol. 4, no. 2, pp. 24–38, 2009.
- 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, vol. 34, no. 7, pp. 947–955, 2011.
- K. Y. Kwon and R. J. Kish, “Technical trading strategies and return predictability: NYSE,” Applied Financial Economics, vol. 12, no. 9, pp. 639–653, 2002.