Processing Uncertainties in Modeling Nonstationary Time Series Using Decision Support Systems

Authors

DOI:

https://doi.org/10.20535/1810-0546.2016.5.77031

Keywords:

Time series forecasting, Systemic approach, Probabilistic, statistical and parametric uncertainties, Decision support system

Abstract

Background. Forecasting of nonlinear nonstationary time series (NNTS) is important problem in economics, marketing, industry, ecology and many other branches of science and practical activities. Successful solution of the problem requires development of modern computer based decision support systems (DSS) capable to generate reliable estimates of forecasts in conditions of uncertainty of various type and origin.

Objective. The purpose of the research is as follows: development of requirements to the modern DSS and their formal representation; analysis of uncertainty types characteristic for model building and forecasting; selection of techniques for taking into consideration of the uncertainties; and illustration of the system application to solving the problem of forecasts estimation for heteroscedastic NNTS using statistical data.

Methods. To reach the objectives stated the following methods were used: systemic approach to statistical data analysis; statistical approach to identification and taking into consideration of possible uncertainties; Kalman filtering techniques; Bayesian programming approach and statistical criteria of model adequacy and quality of forecasts.

Results. Formal description of the DSS is provided, and requirements to its development are given; the classes of mathematical methods necessary for DSS implementation are proposed; some approaches to formal taking into consideration of probabilistic, statistical and parametric uncertainties are discussed; and illustrating example of the DSS application is considered.

Conclusions. Systemic approach to DSS constructing for solving the problem of nonlinear nonstationary time series forecasting turned out to be very fruitful. Using the system proposed it is possible to take into consideration various uncertainties of probabilistic, statistical and parametric type, and to compute high quality estimates of short and medium term forecasts for NNTS. The approach proposed has good perspectives for the future improvements and enhancement.

Author Biographies

Петро Іванович Бідюк, Institute for applied system analysis of the NTUU KPI

Petro I. Bidyuk,

doctor of engineering, professor

Олександр Петрович Гожий, Black Sea State University named after Petro Mohyla

Olexander P. Gozhyj,

PhD, associate professor 

Олександр Миколайович Трофимчук, Institute of Telecommunications and Global Information Sphere at NAS of Ukraine

Oleksandr M. Trofymchuk,

doctor of engineering, professor

Олексій Петрович Бідюк, Profigent Labs

Oleksii P. Bidiuk,

master.

Control software for complex industrial system: research and development

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Published

2016-10-31

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Art