Skip to main content
Have a personal or library account? Click to login
Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence Cover

Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence

International Journal of Applied Mathematics and Computer Science
Volume 22 (2012): Issue 4 (December 2012)
By:  and    
Open Access
|Dec 2012
References

References

  1. Albus, J.S. (1975). Data storage in the cerebellar model articulation controller (CMAC),(2): 228-233.
    Open DOISearch in Google Scholar
  2. Boyacioglu, M.A. and Avci, D. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange,(12): 7908-7912.
    Open DOISearch in Google Scholar
  3. Brdys´, M.A., Borowa, A., Idz´kowiak, P. and Brdys´, M.T. (2009). Adaptive prediction of stock exchange indices by state space wavelet networks,(2): 337-348, DOI: 10.2478/v10006-009-0029-z.
    Open DOISearch in Google Scholar
  4. Buckley, J.J. (1989). Fuzzy complex numbers,(3): 333-345.
    Open DOISearch in Google Scholar
  5. Castro, J.L. (1995). Fuzzy logic controllers are universal approximators,(4): 629-635.
    Open DOISearch in Google Scholar
  6. Chen, Z., Aghakhani, S., Man, J. and Dick, S. (2011). ANCFIS: A neurofuzzy architecture employing complex fuzzy sets,(2): 305-322.
    Open DOISearch in Google Scholar
  7. Deng, X. and Wang, X. (2009). Incremental learning of dynamic fuzzy neural networks for accurate system modeling,(7): 972-987.
    Open DOISearch in Google Scholar
  8. Dick, S. (2005). Toward complex fuzzy logic,(3): 405-414.
    Open DOISearch in Google Scholar
  9. Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory,, pp. 39-43.
    Search in Google Scholar
  10. Gao, Y. and Er, M.J. (2005). Narmax time series model prediction: Feedforward and recurrent fuzzy neural network approaches,(2): 331-350.
    Open DOISearch in Google Scholar
  11. Graves, D. and Pedrycz, W. (2009). Fuzzy prediction architecture using recurrent neural networks,(7-9): 1668-1678.
    Open DOISearch in Google Scholar
  12. Hornik, K., Stinchcombe, M. and White, H. (1989). Multilayer feedforward networks are universal approximators,(5): 359-366.
    Open DOISearch in Google Scholar
  13. Jang, J.S.R. (1993). ANFIS: adaptive-network-based fuzzy inference system,(3): 665-685.
    Open DOISearch in Google Scholar
  14. Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization,, pp. 1942-1948.
    Search in Google Scholar
  15. Khashei, M. and Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting,(2): 2664-2675.
    Open DOISearch in Google Scholar
  16. Kim, J. and Kasabov, N. (1999). HYFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems,(9): 1301-1319.
    Open DOISearch in Google Scholar
  17. Li, C. and Cheng, H.-H. (2011). Intelligent forecasting of S&P 500 time series-A self-organizing fuzzy approach,N.T. Nguyen, C.-G. Kim and A. Janiak (Eds.),, Lecture Notes in Artificial Intelligence, Vol. 6592, Springer-Verlag, Berlin/Heidelberg, pp. 411-420.
    Open DOISearch in Google Scholar
  18. Li, C. and Chiang, T.-W. (2011a). Complex fuzzy computing to time series prediction-A multi-swarm PSO learning approach,N.T. Nguyen, C.-G. Kim and A. Janiak (Eds.), Lecture Notes in Artificial Intelligence, Vol. 6592, Springer-Verlag, Berlin/Heidelberg, pp. 242-251.
    Search in Google Scholar
  19. Li, C. and Chiang, T.-W. (2011b). Complex fuzzy model with PSO-RLSE hybrid learning approach to function approximation,(4): 409-430.
    Open DOISearch in Google Scholar
  20. Li, C. and Chiang, T.-W. (2011c). Function approximation with complex neuro-fuzzy system using complex fuzzy sets-A new approach,(3): 261-276.
    Open DOISearch in Google Scholar
  21. Li, C., Chiang, T.-W., J.-W., H. and Wu, T. (2010). Complex neuro-fuzzy intelligent approach to function approximation,, pp. 151-156.
    Search in Google Scholar
  22. Li, C. and Lee, C.-Y. (2003). Self-organizing neuro-fuzzy system for control of unknown plants,(1): 135-150.
    Open DOISearch in Google Scholar
  23. Li, C., Lee, C.-Y. and Cheng, K.-H. (2004). Pseudoerror-based self-organizing neuro-fuzzy system,(6): 812-819.
    Open DOISearch in Google Scholar
  24. Li, C. and Priemer, R. (1997). Self-learning general purpose PID controller,(2): 167-189.
    Open DOISearch in Google Scholar
  25. Li, C. and Priemer, R. (1999). Fuzzy control of unknown multiple-input-multiple-output plants,(2): 245-267.
    Open DOISearch in Google Scholar
  26. Lu, C.-J., Lee, T.-S. and Chiu, C.-C. (2009). Financial time series forecasting using independent component analysis and support vector regression,(2): 115-125.
    Open DOISearch in Google Scholar
  27. Man, J.Y., Chen, Z. and Dick, S. (2007). Towards inductive learning of complex fuzzy inference systems,, pp. 415-420.
    Search in Google Scholar
  28. Mansour, M.M., Mekhamer, S.F. and El-Kharbawe, N.-S. (2007). A modified particle swarm optimizer for the coordination of directional overcurrent relays,(3): 1400-1410.
    Open DOISearch in Google Scholar
  29. Moody, J. and Darken, C.J. (1989). Fast learning in networks of locally-tuned processing units,(2): 281-294.
    Open DOISearch in Google Scholar
  30. Moses, D., Degani, O., Teodorescu, H.N., Friedman, M. and Kandel, A. (1999). Linguistic coordinate transformations for complex fuzzy sets,, pp. 1340-1345.
    Search in Google Scholar
  31. Mousavi, S.J., Ponnambalam, K. and Karray, F. (2007). Inferring operating rules for reservoir operations using fuzzy regression and ANFIS,(10): 1064-1082.
    Open DOISearch in Google Scholar
  32. Niu, B., Zhu, Y., He, X. and Wu, H. (2007). MCPSO: A multi-swarm cooperative particle swarm optimizer,(2): 1050-1062.
    Open DOISearch in Google Scholar
  33. Paul, S. and Kumar, S. (2002). Subsethood-product fuzzy neural inference system (SUPFUNIS),(3): 578-599.
    Open DOISearch in Google Scholar
  34. Ramot, D., Friedman, M., Langholz, G. and Kandel, A. (2003). Complex fuzzy logic,(4): 450-461.
    Open DOISearch in Google Scholar
  35. Ramot, D., Milo, R., Friedman, M. and Kandel, A. (2002). Complex fuzzy sets,(2): 171-186.
    Open DOISearch in Google Scholar
  36. Rojas, I., Valenzuela, O., Rojas, F., Guillen, A., Herrera, L.J., Pomares, H., Marquez, L. and Pasadas, M. (2008). Soft-computing techniques and ARMA model for time series prediction,(4-6): 519-537.
    Open DOISearch in Google Scholar
  37. Simin´ski, K. (2010). Rule weights in a neuro-fuzzy system with a hierarchical domain partition,(2): 337-347, DOI: 10.2478/v10006-010-0025-3.
    Open DOISearch in Google Scholar
  38. Smetek, M. and Trawinski, B. (2011). Selection of heterogeneous fuzzy model ensembles using self-adaptive genetic algorithms,(3): 309-327.
    Open DOISearch in Google Scholar
  39. Tung, W.L. and Quek, C. (2011). Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach,(5): 4668-4688.
    Open DOISearch in Google Scholar
  40. Vo, N., Quang, T., Dinh, T. and Dinh, T. (2011). Robust visual tracking using randomized forest and online appearance model,N.T. Nguyen, C.-G. Kim and A. Janiak (Eds.),Lecture Notes in Artificial Intelligence, Vol. 6592, Springer-Verlag, Berlin/Heidelberg pp. 212-221.
    Open DOISearch in Google Scholar
  41. Yahoo Finance for Hang Seng Index (2011). Website: http://finance.yahoo.com/q?s="HSI.
    Search in Google Scholar
  42. Yahoo Finance for Nikkei 225 Index (2011). Website, http://finance.yahoo.com/q?s="N225.
    Search in Google Scholar
  43. Yahoo Finance for Taiwan Stock Exchange Capitalization Weighted Stock Index (2011). Website, http://finance.yahoo.com/q?s="TWII.
    Search in Google Scholar
  44. Yuhui, S. and Eberhart, R.C. (2001). Fuzzy adaptive particle swarm optimization,pp. 101-106.
    Search in Google Scholar
  45. Zhang, G., Dillon, T.S., Cai, K.-Y, Ma, J. and Lu, J. (2009). Operation properties and (δ-equalities of complex fuzzy sets,(8): 1227-1249.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.2478/v10006-012-0058-x | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Journal RSS Feed
Language: English
Page range: 787 - 800
Published on: Dec 28, 2012
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
complex fuzzy set,
complex neuro-fuzzy system,
hierarchical multi-swarm particle swarm optimization,
recursive least squares estimator,
time series forecasting.
Related subjects:
Mathematics,
Applied mathematics

© 2012 Chunshien Li, Tai-Wei Chiang, published by University of Zielona Góra
This work is licensed under the Creative Commons License.

Volume 22 (2012): Issue 4 (December 2012)
Next article