메뉴 건너뛰기




Volumn , Issue , 2008, Pages 212-249

Generalized correlation higher order neural networks for financial time series prediction

Author keywords

[No Author keywords available]

Indexed keywords


EID: 84900574356     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59904-897-0.ch010     Document Type: Chapter
Times cited : (6)

References (26)
  • 1
    • 0030150518 scopus 로고    scopus 로고
    • A novel neural hetero-associative memory model for pattern recognition
    • Bandyopadhyay, S., Datta, AK. (1996). A novel neural hetero-associative memory model for pattern recognition. Pattern Recognition, 29(5), 789-795.
    • (1996) Pattern Recognition , vol.29 , Issue.5 , pp. 789-795
    • Bandyopadhyay, S.1    Datta, A.K.2
  • 2
    • 84901514568 scopus 로고    scopus 로고
    • Bank of England, Retrieved June 7, 2007. from
    • Bank of England (2007). Retrieved June 7, 2007. from http://www.bankofengland.co.uk/
    • (2007)
  • 5
    • 33746606018 scopus 로고    scopus 로고
    • A new computation method of input selection for stock marker forecasting with neural networks
    • Part 4
    • Huang, W., Wang, S. Y., Yu, L., Bao, Y. K. & Wang, L. (2006). A new computation method of input selection for stock marker forecasting with neural networks. Computational Science Proceedings, ICCS 2006, Part 4, 3994, 308-315
    • (2006) Computational Science Proceedings, ICCS 2006 , vol.3994 , pp. 308-315
    • Huang, W.1    Wang, S.Y.2    Yu, L.3    Bao, Y.K.4    Wang, L.5
  • 6
    • 0027262895 scopus 로고
    • Multi-layer feedforward networks with a non-polynomial activation can approximate any function
    • Leshno, M., Lin, V., Pinkus, A., & Schoken, S. (1993). Multi-layer feedforward networks with a non-polynomial activation can approximate any function. Neural Networks, 6, 861-867.
    • (1993) Neural Networks , vol.6 , pp. 861-867
    • Leshno, M.1    Lin, V.2    Pinkus, A.3    Schoken, S.4
  • 8
    • 0000169232 scopus 로고
    • An algorithm for least-squares estimation of nonlinear parameters
    • Marquardt, D. (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431-441.
    • (1963) SIAM J. Appl. Math , vol.11 , pp. 431-441
    • Marquardt, D.1
  • 11
    • 0005462359 scopus 로고
    • Digital neural networks, matched filters and optical implementations
    • In Aleksander, I. (Ed.), Kogan Page, North Oxford Academic Publishers Ltd
    • Midwinter, J.E., & Selviah, D.R. (1989). Digital neural networks, matched filters and optical implementations. In Aleksander, I. (Ed.) Neural Computing Architectures (pp. 258-278). Kogan Page, North Oxford Academic Publishers Ltd.
    • (1989) Neural Computing Architectures , pp. 258-278
    • Midwinter, J.E.1    Selviah, D.R.2
  • 12
    • 0027205884 scopus 로고
    • A scaled conjugate gradient algorithm for fast supervised learning
    • Moller. (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6(4), pp.525-533.
    • (1993) Neural Networks , vol.6 , Issue.4 , pp. 525-533
    • Moller1
  • 13
  • 14
    • 84901537604 scopus 로고    scopus 로고
    • th Sept 2007 from
    • th Sept 2007 from http://www.financewise.com/public/edit/riskm/interestrate/interestraterisk00-models.htm
    • (2000)
  • 20
    • 84900617386 scopus 로고    scopus 로고
    • Invited paper: Similarity suppression algorithm for designing pattern discrimination filters
    • Selviah, D. R., & Stamos, E. (2002). Invited paper: Similarity suppression algorithm for designing pattern discrimination filters. Asian Journal of Physics, 11(3), 367-389.
    • (2002) Asian Journal of Physics , vol.11 , Issue.3 , pp. 367-389
    • Selviah, D.R.1    Stamos, E.2
  • 21
    • 84900599874 scopus 로고    scopus 로고
    • Feature enhancement and similarity suppression algorithm for noisy pattern recognition
    • In D. P. Casasent, & T. H. Chao (Eds.), Orlando, USA: SPIE
    • Stamos, E., & Selviah, D. R. (1998). Feature enhancement and similarity suppression algorithm for noisy pattern recognition. In D. P. Casasent, & T. H. Chao (Eds.), Optical Pattern Recognition IX (pp 182-189). Orlando, USA: SPIE.
    • (1998) Optical Pattern Recognition IX , pp. 182-189
    • Stamos, E.1    Selviah, D.R.2
  • 23
    • 84901537137 scopus 로고    scopus 로고
    • th Sept 2007
    • th Sept 2007, http://www.snowgold.com/financial/fingloss.html
    • (2007)
  • 24
    • 0034894378 scopus 로고    scopus 로고
    • An empirical analysis of data requirements for financial forecasting with neural networks
    • Walczak S. (2001). An empirical analysis of data requirements for financial forecasting with neural networks. Journal of Management Information Systems, 17(3), 203-222.
    • (2001) Journal of Management Information Systems , vol.17 , Issue.3 , pp. 203-222
    • Walczak, S.1
  • 25
    • 0001126233 scopus 로고    scopus 로고
    • Heuristic principles for the design of artificial neural networks
    • Walczak, S., & Cerpa, N. (1999) Heuristic principles for the design of artificial neural networks. Information and Software Technology, 41(2), 109-119.
    • (1999) Information and Software Technology , vol.41 , Issue.2 , pp. 109-119
    • Walczak, S.1    Cerpa, N.2
  • 26
    • 84901522068 scopus 로고    scopus 로고
    • Yahoo!, Retrieved May 3, 2007, from
    • Yahoo! (2007). Finance. Retrieved May 3, 2007, from http://finance.yahoo.com
    • (2007) Finance


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.