Empirical analysis of model selection criteria for genetic programming in modeling of time series system

Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

Volumn , Issue , 2013, Pages 90-94

  Garg, A ^a   Sriram, S ^a   Tai, K ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

fitness function; genetic programming; model selection; stock market

Indexed keywords

FITNESS FUNCTIONS; GENE EXPRESSION PROGRAMMING; GENERALIZATION ABILITY; MODEL SELECTION; MODEL SELECTION CRITERIA; MULTI EXPRESSION PROGRAMMING; NEW YORK STOCK EXCHANGE; STOCK MARKET;

ARTIFICIAL INTELLIGENCE; COMMERCE; FINANCE; GENETIC PROGRAMMING;

ECONOMICS;

EID: 84886052287     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIFEr.2013.6611702     Document Type: Conference Paper

References (33)

1. Wagner, N., et al., Time series forecasting for dynamic environments: the DyFor genetic program model. Evolutionary Computation, IEEE Transactions on, 2007. 11(4): p. 433-452.
2. Iba, H. and N. Nikolaev. Genetic programming polynomial models of financial data series. in Evolutionary Computation, 2000. Proceedings of the 2000 Congress on. 2000. IEEE.
3. El-Telbany, M. E. The Egyptian stock market return prediction: A genetic programming approach. in International Conference on Electrical, Electronic and Computer Engineering. 2005.
4. Santini, M. and A. Tettamanzi, Genetic programming for financial time series prediction. Genetic Programming, 2001: p. 361-370.
5. Wagner, N. and Z. Michalewicz. Genetic programming with efficient population control for financial time series prediction. in 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers. 2001.
6. Kaboudan, M., Genetic programming prediction of stock prices. Computational Economics, 2000. 16(3): p. 207-236.
7. Iba, H. and T. Sasaki. Using genetic programming to predict financial data. in Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on. 1999. IEEE.
8. Grosan, C. and A. Abraham, Stock market modeling using genetic programming ensembles. Genetic Systems Programming, 2006: p. 131-146.
9. Chen, F., Y. H. Chen, and J. Z. Zhang, Stock index modeling using IP based gene expression programming. Jisuanji Gongcheng yu Yingyong(Computer Engineering and Applications), 2007. 42(26): p. 227-229.
10. Qian, X. and C. Yang, Improved gene expression programming algorithm tested by predicting stock indexes. CAAI Transactions on Intelligent Systems, 2010. 4: p. 005.
11. Grosan, C., et al. Stock market prediction using multi expression programming. in Artificial intelligence, 2005. epia 2005. portuguese conference on. 2005. IEEE.
12. Kwon, Y. K. and B. R. Moon. Evolutionary ensemble for stock prediction. in Genetic and Evolutionary Computation-GECCO 2004. 2004. Springer.
13. Oltean, M. and C. Grosan, A comparison of several linear genetic programming techniques. Complex Systems, 2003. 14(4): p. 285-314.
14. Hsu, C. M., A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming. Expert systems with applications, 2011. 38(11): p. 14026-14036.
15. Chen, S. H., Genetic algorithms and genetic programming in computational finance2002: Springer.
16. de Souza, L. V., A. T. R. Pozo, and A. C. Neto. Using correlation to improve boosting technique: an application for time series forecasting. in Tools with Artificial Intelligence, 2006. ICTAI'06. 18th IEEE International Conference on. 2006. IEEE.
17. Akaike, H., An information criterion (AIC). Math Sci, 1976. 14(153): p. 5-9.
18. Schwarz, G., Estimating the dimension of a model. The annals of statistics, 1978. 6(2): p. 461-464.
19. Barron, A. R., Predicted squared error: a criterion for automatic model selection. 1984.
20. Miller, A., Subset selection in regression2002: Chapman & Hall/CRC.
21. Moody, J., Prediction risk and architecture selection for neural networks. NATO ASI SERIES F COMPUTER AND SYSTEMS SCIENCES, 1994. 136: p. 147-147.
22. Vapnik, V. N., Statistical learning theory. 1998.
23. Cherkassky, V. and Y. Ma, Comparison of model selection for regression. Neural computation, 2003. 15(7): p. 1691-1714.
24. Koza, J. R., Genetic programming II: automatic discovery of reusable programs. 1994.
25. Koza, J. R., Genetic programming II: automatic discovery of reusable programs1994.
26. Hinchliffe, M., et al. Modelling chemical process systems using a multi-gene genetic programming algorithm. in Genetic Programming: Proceedings of the First Annual Conference (late breaking papers). 1996.
27. Hiden, H. G., Data-based modelling using genetic programming, 1998, University of Newcastle upon Tyne.
28. Searson, D. P., D. E. Leahy, and M. J. Willis. GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. in Proceedings of the International MultiConference of Engineers and Computer Scientists. 2010. Citeseer.
29. Garg, A. & Tai, K. 2011. A Hybrid Genetic Programming-Artificial Neural Network Approach For Modeling of Vibratory Finishing Process. International Proceedings of Computer Science and Information Technology (IPCSIT), Vol. 18, pp. 14-19.
30. Garg, A. & Tai, K. 2012. Comparison of regression analysis, Artificial Neural Network and genetic programming in Handling the multicollinearity problem. Proceedings of International Conference on Modelling, Identification & Control (ICMIC). IEEE, pp. 353-358.
31. Garg, A. and Tai, K. 2012. Review of Genetic Programming in modeling of machining processes. International Conference on Modelling, Identification and Control (ICMIC), IEEE, pp. 653-658
32. Baziar, M. H., et al., Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach. Computers & Geosciences, 2011. 37(11): p. 1883-189.
33. Zhang, W., Z. Wu, and G. Yang, Genetic programming-based chaotic time series modeling. Journal of Zhejiang University Science A, 2004. 5(11): p. 1432-1439.