Stock market forecasting using ANFIS with OWA operator

International Journal of Artificial Intelligence

Volumn 12, Issue 2, 2014, Pages 102-114

  Kaur, Gurbinder ^a   Dhar, Joydip ^b   Guha, Rangan K ^a  

^a SANT LONGOWAL INSTITUTE OF ENGINEERING AND TECHNOLOGY   (India)

^b ABV INDIAN INSTITUTE OF INFORMATION TECHNOLOGY AND MANAGEMENT   (India)

Author keywords

Forecasting; Fuzzy logic; Neural networks; OWA; Stock market; Subtractive clustering

Indexed keywords

EID: 84936987996     PISSN: 09740635     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. Aisjah, A. S. and Arifin, S. 2013. Maritime weather prediction using fuzzy logic in java sea for shipping feasibility, International Journal of Artificial Intelligence 10: 112-122.
2. Ansari, T., Kumar, M., Shukla, A., Dhar, J. and Tiwari, R. 2010. Sequential combination of statistics, econometrics and adaptive neural-fuzzy interface for stock market prediction, Expert System with Applications 37: 5116-5125.
3. Avci, E. and Akpolat, Z. H. 2006. Speech recognition using a wavelet packet adaptive network based fuzzy inference system, Expert System with Applications 31: 495-503.
4. Bollerslev, T. 1986. Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics 31: 307-327.
5. Box, G. E. P. and Jenkins, G. M. 1997. Time Series Analysis: Forecasting and Control, 3rd edition, Prentice Hall, Englewood Cliffs.
6. Champernowne, D. G. 1948. Sampling theory applied to autoregressive schemes, Journal of the Royal Statistical Society: Series B 10: 204-231.
7. Chang, P. C., Liu, C. H. and Fan, C. Y. 2009. Data clustering and fuzzy neural network for sales forecasting: a case study in printed circuit board industry, Knowledge-Based Systems 22: 344-355.
8. Chen, S. M. 1996. Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems 81: 311-319.
9. Chen, T. L., Cheng, C. H. and Teoh, H. J. 2008. High-order fuzzy time-series based on multiperiod adaptation model for forecasting stock market, Physica A 387: 876-888.
10. Cheng, C. H., Wei, L. Y., Liu, J. W. and Chen, T. L. 2013. Owa based anfis model for taiex forecasting, Economic Modelling 30: 442-448.
11. Chiu, S. 1994. Fuzzy model identification based on cluster estimation, Journal of Intelligent and Fuzzy Systems 2: 267-278.
12. David, R. C., Dragos, C. A., Bulzan, R. G., Precup, R. E., Petriu, E. M. and Radac, M. B. 2012. An approach to fuzzy modeling of magnetic levitation systems, International Journal of Artificial Intelligence 9: 1-18.
13. Engle, R. F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation, Econometrica 50: 987-1008.
14. Feriyonika and Dewantoro, G. 2013. Fuzzy sliding mode control for enhancing injection velocity performance in injection molding machine, International Journal of Artificial Intelligence 10: 75-87.
15. Fuller, R. and Majlender, P. 2001. An analytic approach for obtaing maximal entropy owa operator weights, Fuzzy Sets and Systms 124: 53-57.
16. Fuller, R. and Majlender, P. 2003. On obtaining minimal variability owa operator weights, Fuzzy Sets and Systems 136: 203-215.
17. Hadavandi, E., Shavandi, H. and Ghanbari, A. 2010. Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting, Knowledge-Based Systems 23: 800-808.
18. Jang, J. S. R. 1993. Anfis: Adaptive network based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics 23: 665-684.
19. Jang, J. S. R., Sun, C. T. and Mizutani, E. 1997. Neuro fuzzy and soft computing, Matlab Curriculum Series, Prentice-Hall Englewood Cliffs.
20. Jilani, T. A. and Burney, S. M. A. 2008. A refined fuzzy time series model for stock market forecasting, Physica A 387: 2857-2862.
21. Joelianto, E., Canura, D. C. and Priyanto, M. P. 2013. Anfis-hybrid reference control for improving transient response of controlled systems using pid controller, International Journal of Artificial Intelligence 10: 88-111.
22. Keles, A., Kolcak, M. and Keles, A. 2008. The adaptive neuro-fuzzy model for forecasting the domestic debt, Knowledge-Based Systems 21: 951-957.
23. Kim, K. J. and Lee, W. B. 2004. Stock market prediction using artificial neural networks with optimal feature transformation, Neural Computing and Applications 13: 255-260.
24. Kodogiannis, V. and Lolis, A. 2002. Forecasting financial time series using neural network and fuzzy system-based techniques, Neural Computing and Applications 11: 90102.
25. O’Connor, N. and Madden, M. G. 2006. A neural network approach to predicting stock exchange movements using external factors, Knowledge-Based Systems 19: 371-378.
26. Wei, L. Y., Chen, T. L. and Ho, T. H. 2011. A hybrid model based on adaptive-network-based fuzzy inference system to forecast taiwan stock market, Expert Systems with Applications 38: 13625-13631.
27. Yager, R. R. 1988. On ordered weighted averaging aggeriation operators in multi-criteria decision making, IEEE Transactions on Systems, Man, and Cybernetics 18: 183-190.
28. Yang, L., Dawson, C. W., Brown, M. R. and Gell, M. 2006. Neural network and ga approaches for dwelling fire occurrence prediction, Knowledge-Based Systems 19: 213-219.