Stock Price Forecasting: Statistical, Classical and Fuzzy Neural Network Approach

  • Dušan Marček
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3131)


An AR model, a classical neural feedforward network and an artificial fuzzy neural network based on B-spline member ship functions are presented and considered. Some preliminary results and further experiments that we performed are presented.


Neural and fuzzy neural networks B-spline functions Autoregressive models 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bayhan, G.M.: Sales Forecasting Using Adaptive Signal Processing Algorithms. Neural Network World 7(4-5), 579–589 (1997)Google Scholar
  2. 2.
    Box, G.E., Jenkins, G.M.: Time Series Analysis, Forecasting and Control. Holden- Day, San Francisco (1976)zbMATHGoogle Scholar
  3. 3.
    Darbellay, G.: A Non-Linear Correlation Measure and its Application to Financial Time Series. Neural Network World 5(4), 401–405 (1955)Google Scholar
  4. 4.
    Gorr, W.L., Nagin, D.N., Szcypula, J.: Comparative Study of Artificial Neural Network and Statistical Models for Predicting Student Grade Point Averages. International Journal of Forecasting 10, 17–34 (1994)CrossRefGoogle Scholar
  5. 5.
    Kecman, V.: Learning ang sioft computing: support vector machines, neural networks, and fuzzy logic models. Massechusetts Institute of Technology (2001)Google Scholar
  6. 6.
    Marček, D.: Stock Price Prediction Using Autoregressive Models and Signal Processing Procedures. Proceedings of the 16th Conference MME 1998, Cheb 8.-10.9.1998, pp. 114-121 (1998) Google Scholar
  7. 7.
    Polycarpou, M.M., Ioannou, P.A.: Learning and covergence analysis of neural-type structured networks. IEE Transactions on Neural Networks 3, 39–50 (1992)CrossRefGoogle Scholar
  8. 8.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE trans. System Man. Cybernet,16 yr ,116–132 (1985)Google Scholar
  9. 9.
    Tsay, R.S.: Nonlinearity Tests for Time Series. Biometrika 73(2), 461–466 (1986)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Wu, Z.Q., Harris, C.J.: Indirect Adaptive Neurofuzzy Estimation of Nonlinear Time Series. Neural Network World 3/96, 407–416 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Dušan Marček
    • 1
  1. 1.The Faculty of Management Science and InformaticsUniversity of ŽilinaŽilinaSlovakia

Personalised recommendations