Systemic Approach to Forecasting




Uncertainties in modeling and forecasting, Systematic approach, Decision support system


Background. Further enhancement of forecasts quality for dynamics of financial and economic processes requires development of new techniques and approaches in the frames of modern concepts for constructing informational decision support systems (DSS).

Objective. The main purpose of the study is as follows: to consider system analysis principles that are suitable for solving the problem of short-term forecasting; to develop effective data processing system that implements the system analysis principles selected in the frames of DSS; to analyze possible types of uncertainties that are encountered in model constructing and forecasts estimating, and to propose the methods for their description and taking into consideration.

Methods. To develop DSS for forecasting financial and economic processes and estimation of financial risks the following system analysis principles were hired: hierarchical architecture, the possibilities for identification and processing possible uncertainties, alternatives computing, and tracking the computational procedures for all stages of data processing. The system developed provides possibilities for taking into consideration statistical and parametric uncertainties. The DSS proposed has a modular architecture that could be easily expanded with new functions like preliminary data processing, model parameters estimation, and procedures for computing forecasts and financial risks.

Results. The main result of the study is systemic methodology of mathematical modeling financial and economic processes, that has been implemented in the frames of the DSS proposed. High quality of final results is achieved thanks to appropriate tracking of all computations using several sets of statistical quality criteria. An example is given for mathematical modeling, estimation and forecasting of financial risk. The results of estimation show that the systemic approach proposed has good perspectives for its practical use.

Conclusions. Thus, we proposed a systemic approach to mathematical modeling and forecasting financial and economic processes as well as estimation of financial risk. The use of the approach provides possibilities for computing estimate forecasts of high quality using statistical data.

Author Biographies

Петро Іванович Бідюк, NTUU "KPI"

Petro I. Bidyuk,

Doctor of engineering, professor

Олександр Миколайович Трофимчук, The Institute of Telecommunications and Global Information Space of the NASU

Oleksandr M. Trofymchuk,

Doctor of engineering, professor

Олексій Петрович Бідюк, Profigent Labs

Oleksii P. Bidiuk,



G. Fernandez, Data Mining Using SAS Applications. New York: CRC Press LLC, 2003, 360 p.

P.I. Bidyuk et al., Time Series Analysis. Kyiv, Ukraine: Polytechnika, 2013, 600 p. (in Ukrainian).

M.Z. Zgurovskii and N.D. Pankratova, System Analysis: Problems, Methodology, Applications. Kyiv, Ukraine: Naukova Dumka, 2005, 745 p. (in Russian).

V.S. Anfilatov et al., System Abalysis in Management.Moscow, Russia: Finances and Statistica, 2002, 368 p. (in Russian).

M.Z. Zgurovskii and V.N. Podladchikov, Analytical Methods of Kalman Filtering. Kyiv, Ukraine: Naukova Dumka, 1995, 285 p. (in Russian).

B.P. Gibbs, Advanced Kalman Filtering, Least Squares and Modeling. Hoboken: John Wiley & Sons, Inc., 2011, 627 p.

S. Haykin, Adaptive Filter Theory. Upper Saddle River (New Jersey): Prentice Hall, 2002, 922 p.

W.R. Gilks et al., Markov Chain Monte Carlo in Practice. New York: CRC Press LLC, 2000, 486 p.

C.S. Jao, Efficient Decision Support SystemsPractice and Challenges from Current to Future. Rijeka (Croatia): Intech, 2011, 556 p.

M.Z. Zgurowskii et al., “Methods of constructing Bayesian networks based on scoring functions”, Cybernetics and System Analysis, vol. 44, no. 2, pp. 219–224, 2008.