A method of preliminary forecasting of time series of financial data / Shatashvili, / Didmanidze, / Kakhiani, / Fomina. (2020)
Ukrainian

English  Cybernetics and Systems Analysis   /     Issue (2020, 56 (2))

Shatashvili A.D., Didmanidze I.S., Kakhiani G.A., Fomina T.A.
A method of preliminary forecasting of time series of financial data

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 and to determine its type. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: fractal, neural networks, time series, Costs, Financial markets, Time series, Correlation structure, Daily fluctuations, Financial data, Initial time, Long term memory, Market instruments, Methods of analysis, Stationary models, Time series analysis


Cite:
Shatashvili A.D., Didmanidze I.S., Kakhiani G.A., Fomina T.A. (2020). A method of preliminary forecasting of time series of financial data. Cybernetics and Systems Analysis, 56 (2), 149–156. doi: https://doi.org/10.1007/s10559-020-00245-6 http://jnas.nbuv.gov.ua/article/UJRN-0001103879 [In Russian].


 

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