An efficient CMAC neural network for stock index forecasting

Expert Systems with Applications

Volumn 38, Issue 12, 2011, Pages 15194-15201

  Lu, Chi Jie ^a   Wu, Jui Yu ^b  

^a CHING YUN UNIVERSITY   (Taiwan)

^b LUNGHWA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Back propagation neural network; Cerebellar model articulation controller; Neural network; Stock index forecasting; Support vector regression

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; CEREBELLAR MODEL ARTICULATION CONTROLLER; CMAC NEURAL NETWORK; FAST LEARNING; FORECASTING PERFORMANCE; FORECASTING PROBLEMS; GENERALIZATION CAPABILITY; GENERALIZATION ERROR; INVESTMENT DECISIONS; NOISE RESISTANCE; PREDICTION ACCURACY; PRIVATE INVESTORS; QUANTIZATION RESOLUTION; ROBOT CONTROLS; ROBUSTNESS EVALUATION; STOCK EXCHANGE; STOCK INDEX FORECASTING; STOCK INDICES; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS;

BACKPROPAGATION; FORECASTING; INVESTMENTS; PRIVATIZATION; TORSIONAL STRESS;

NEURAL NETWORKS;

EID: 80052026368     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.05.082     Document Type: Article

References (54)

1. J.S. Albus Data storage in the cerebellar model articulation controller (CMAC) ASME Journal of Dynamic Systems, Measurement, and Control 97 1975 228 233
2. J.S. Albus A new approach to manipulator control: The cerebellar model articulation controller (CMAC) ASME Journal of Dynamic Systems, Measurement, and Control 97 1975 220 227
3. G.S. Atsalakis, and K.P. Valavanis Surveying stock market forecasting techniques - part II: Soft computing methods Expert Systems with Applications 36 2009 5932 5941
4. K.G. Balachandher, M.N. Fauzias, and M.M. Lai An examination of the random walk model and technical trading rules in the Malaysian stock market Quarterly Journal of Business and Economics 41 2002 81 104
5. L. Cao Support vector machines experts for time series forecasting Neurocomputing 51 2003 321 339
6. L. Cao, and F.E.H. Tay Financial forecasting using support vector machines Neural Computing & Applications 10 2001 184 192
7. M. Castro-Neto, Y.S. Jeong, M.K. Jeong, and L.D. Han Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions Expert Systems with Applications 36 2009 6164 6173
8. Chang, C. C., & Lin, C. J. (2001). LIBSVM: A library for support vector machines. .
9. C. Chatfield Time-series forecasting 2001 Chapman & Hall, CRC New York, USA
10. Y. Chauvin, and D.E. Rumelhart Backpropagation: theory, architectures, and applications 1995 Lawrence Erlbaum Associates New Jersey
11. V. Cherkassky, and Y. Ma Practical selection of SVM parameters and noise estimation for SVM regression Neural Networks 17 2004 113 126
12. J.G. De Gooijer, and R.J. Hyndman 25 Years of time series forecasting International Journal of Forecasting 22 2006 443 473
13. J.W. Hall Adaptive selection of US stocks with neural nets G.J. Deboeck, Trading on the edge: Neural, genetic and fuzzy systems for chaotic financial markets 1994 Willey New York
14. D.A. Handelman, S.H. Lane, and J.J. Gelfand Integrating neural networks and knowledge-based systems for intelligent robotic control IEEE Control Systems Magazine 10 1990 77 87
15. S. Haykin Neural network: A comprehensive foundation 1999 Prentice Hall New Jersey
16. S.H. Hsu, J.J.P.-A. Hsieh, T.C. Chih, and K.C. Hsu A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression Expert Systems with Applications 36 2009 7947 7951
17. C.L. Huang, and C.Y. Tsai A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting Expert Systems with Applications 36 2009 1529 1539
18. C.P. Hung, and M.H. Wang Diagnosis of incipient faults in power transformers using CMAC neural network approach Electric Power Systems Research 71 2004 235 244
19. K.J. Kim Financial time series forecasting using support vector machines Neurocomputing 55 2003 307 319
20. M. Lawrence, P. Goodwin, M. O'Connor, and D. Önkal Judgmental forecasting: A review of progress over the last 25 years International Journal of Forecasting 22 2006 493 518
21. J. Lee Measurement of machine performance degradation using a neural network model Computers in Industry 30 1996 193 209
22. T.S. Lee, and N.J. Chen Investigating the information content of non-cash-trading index futures using neural networks Expert Systems with Applications 22 2002 225 234
23. T.S. Lee, and C.C. Chiu Neural network forecasting of an opening cash price index International Journal of Systems Science 33 2002 229 237
24. W. Leigh, R. Hightower, and N. Modani Forecasting the New York stock exchange composite index with past price and interest rate on condition of volume spike Expert Systems with Applications 28 2005 1 8
25. C.M. Lin, L.Y. Chen, and C.H. Chen RCMAC hybrid control for MIMO uncertain nonlinear systems using sliding-mode technology IEEE Transactions on Neural Networks 18 2007 708 720
26. Lin, C. J., Hsu, C. W., & Chang, C. C. (2003). A practical guide to support vector classification. Taipei, Taiwan: Department of Computer Science and Information Engineering, National Taiwan University.
27. C.J. Lin, J.H. Lee, and C.Y. Lee A novel hybrid learning algorithm for parametric fuzzy CMAC networks and its classification applications Expert Systems with Applications 35 2008 1711 1720
28. C.M. Lin, Y.F. Peng, and C.F. Hsu Robust cerebellar model articulation controller design for unknown nonlinear systems IEEE Transactions on Circuits and Systems II: Express Briefs 51 2004 354 358
29. C.J. Lu, T.S. Lee, and C.C. Chiu Financial time series forecasting using independent component analysis and support vector regression Decision Support Systems 47 2009 115 125
30. H.C. Lu, and T.Y. Tseng Design and implementation of CMAC-based controller for permanent magnet synchronous motor Electric Power Components and Systems 33 2005 1015 1037
31. P.D. McNelis Neural networks in finance: Gaining predictive edge in the market 2004 Academic Press New York
32. Miller, W. T., Glanz, F. H., & Kraft, L. G. (1990). CMAC: An associative neural network alternative to backpropagation. In Proceedings of the IEEE (Vol. 78, pp. 1561-1567).
33. M.A. Mohandes, T.O. Halawani, S. Rehman, and A.A. Hussain Support vector machines for wind speed prediction Renewable Energy 29 2004 939 947
34. P.F. Pai, and C.S. Lin A hybrid ARIMA and support vector machines model in stock price forecasting Omega 33 2005 497 505
35. P.F. Pai, S.L. Yang, and P.T. Chang Forecasting output of integrated circuit industry by support vector regression models with marriage honey-bees optimization algorithms Expert Systems with Applications 36 2009 10746 10751
36. Y.F. Peng Robust intelligent sliding model control using recurrent cerebellar model articulation controller for uncertain nonlinear chaotic systems Chaos, Solitons & Fractals 39 2009 150 167
37. Qiaolin, D., Jing, T., & Jianxin, L. (2005). Application of new FCMAC neural network in power system marginal price forecasting. In The 7th international power engineering conference, Singapore (pp. 1-57).
38. N.R. Rigozo, E. Echer, D.J.R. Nordemann, L.E.A. Vieira, and H.H. de Faria Comparative study between for classical spectral analysis methods Applied Mathematics and Computation 168 2005 411 430
39. Shi, D. M., Gao, J. B., & Tilani, R. (2004). Univariate time series forecasting with fuzzy CMAC. In 2004 international conference on machine learning and cybernetics, Singapore (Vol. 7, pp. 4166-4170).
40. F.E.H. Tay, and L. Cao Application of support vector machines in financial time series forecasting Omega 29 2001 309 317
41. F.E.H. Tay, and L.J. Cao Support vector machine with adaptive parameters in financial time series forecasting IEEE Transactions on Neural Networks 14 2003 1506 1518
42. U. Thissen, R. van Brakel, A.P. de Weijer, W.J. Melssen, and L.M.C. Buydens Using support vector machines for time series prediction Chemometrics and Intelligent Laboratory Systems 69 2003 35 49
43. V.N. Vapnik An overview of statistical learning theory IEEE Transactions on Neural Networks 10 1999 988 999
44. V.N. Vapnik The nature of statistical learning theory 2000 Springer New York
45. A. Vellido, P.J.G. Lisboa, and J. Vaughan Neural networks in business: A survey of applications (1992-1998) Expert Systems with Applications 17 1999 51 70
46. S. Wang, and Z. Jiang Valve fault detection and diagnosis based on CMAC neural networks Energy and Buildings 36 2004 599 610
47. C. Wen, T.C. Lin, K.C. Chang, and C.H. Huang Classification of ECG complexes using self-organizing CMAC Measurement 42 2009 399 407
48. Y.F. Wong, and A. Sideris Learning convergence in the cerebellar model articulation controller IEEE Transactions on Neural Networks 3 1992 115 121
49. S. Wood Float analysis: Powerful technical indicators using price and volume 2002 John Wiley and Sons New York
50. J.Y. Wu MIMO CMAC neural network classifier for solving classification problems Applied Soft Computing 11 2011 2326 2333
51. S.A.M. Yaser, and A.F. Atiya Introduction to financial forecasting Applied Intelligence 6 1996 205 213
52. G. Zhang, B.E. Patuwo, and M.Y. Hu Forecasting with artificial neural networks: The state of art International Journal of Forecasting 14 1998 35 62
53. Zhou, H., Chen, J., Wu, H., & Ho, S. L. (2003). CMAC-based short-term electricity price forecasting. In Sixth international conference on advances in power system control, operation and management, Hong Kong (Vol. 1, pp. 348-353).
54. Zobrist, A. L. (1969). A new hashing method with application for game playing. Madison, Wisconsin, USA: Computer Sciences Department, University of Wisconsin.