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Cybernetics And Systems Analysis
International Theoretical Science Journal
UDC 519.21
A.D. Shatashvili1, I.Sh. Didmanidze2, G.A. Kakhiani3, T.A. Fomina4


1 Batumi Shota Rustaveli State University, Georgia

shatal@bk.ru

2 Batumi Shota Rustaveli State University, Georgia

ibraim.didmanidze@bsu.edu.ge; ibraimd@mail.ru

3 Batumi Shota Rustaveli State University, Georgia

gregory.kakhiani@bsu.edu.ge

4 Batumi Shota Rustaveli State University, Georgia

shatal@bk.ru

A METHOD OF PRELIMINARY FORECASTING OF TIME SERIES OF FINANCIAL DATA

Abstract. The problem of forecasting the time series of stock prices of leading global companies that are characterized by long-term memory is considered. It is assumed that ignoring the presence of such a correlation structure in time series using traditional methods of analysis leads to a much greater error than taking into account long-term memory in its actual absence. It is assumed that the daily fluctuations in prices for financial market instruments are the Hurst process, that is, they have long-term memory, which means such a time series cannot be effectively analyzed using traditional stationary models that completely ignore this fact. Thus, the task is set, using the R/S analysis method, to determine the presence of long-term memory in the initial time series, to determine its type.

Keywords: data series, fractal, artificial neural networks.



FULL TEXT

REFERENCES

  1. Peters E.E. Chaos and order in the capital markets: A new view of cycles, prices, and market violability. Wiley Finance, 1996. 288 p.

  2. Feder J. Fractals. Premium Press, 1990. 283 p.

  3. Mandelbrot В. The fractal geometry of nature. New York: W.H. Freeman, 1983. 270 p.

  4. Didmanidze I.Sh., Kakhiani G.A. Artificial neural network and stock market investment management. Theoretical and Applied Aspects of Software Systems Construction. TAAPSD'2013: Conference Proceedings. Yalta, 2013. C. 52–53.
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