Investigation of Intelligent Technologies for Formation Forecasting Models

  • Authors

    • Elena Skakalina
    • . .
  • forecasting, genetic algorithm, information technologies, neuro-network group method of data handling, theory of fuzzy sets
  • Actually much attention is paid to the development of new intelligent information technologies for solving forecasting problems in different subject areas. The goal of solving the problem of forecasting dynamic indicators is in most cases to increase the effectiveness of making managerial decisions in conditions of uncertainty for complex distributed systems, which include economic entities. The modern global business environment dynamically forms new markets, which in turn require the use of new innovative technologies, without which it is impossible to have a competitive efficient economy in general and successful business groups in particular. In paper the research of intellectual information technologies of construction of predictive models on the basis of modified adaptive prediction methods is carried out: a neuro-network group method of  data handling and a hybrid genetic algorithm with fuzzy predictive block with the purpose of justification of their use for different subject areas. Exactly  these technologies  are relevant and promising for improving the accuracy of forecasts.


  • References

    1. [1] Ivakhnenko A.G., Ivakhnenko G.A. The Review of Problems Solvable by Algorithms of the Group Method of Data Handling. Published in Pattern Recognition and Image Analysis, Vol. 5, No. 4, 1995, pp.527-535.

      [2] Peters E. Fractal Market Analysis. Applying Chaos Theory to Investment&Economics. / E Peters – J. Wiley&Sons, Inc. – New York, 1994.

      [3] Chen S.M. Forecasting enrolments based on fuzzy time series. – Fuzzy sets Systems, 1996, vol. 81, №3, p.p. 311−319.

      [4] Goldberg, D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass., 1989.

      [5] Korablev, N.M. Parallel immune algorithm of short-term forecasting based on model of clonal selection / N.M. Korablev, G.S. Ivaschenko // Radio Electronics, Computer Science, Control: scientific journal. – 2014. – № 2(31). – Pp. 73-78. ISSN 1607-3274.

      [6] R. Bellman and L. Zadeh, Decision-making in vague terms, Questions analysis and decision-making procedures. Moscow, Russia: Mir, 1976, p. 172–215.

      [7] L.A. Zadeh, J. Kacprzyk (Eds.) Fuzzy logic for the management of uncertainty. Wiley, New York, 1992.

      [8] / < / Data. United States> [lastaccessdon24thApril2018].

  • Downloads

  • How to Cite

    Skakalina, E., & ., . (2018). Investigation of Intelligent Technologies for Formation Forecasting Models. International Journal of Engineering & Technology, 7(3.2), 413-418.