Background. Today there is a problem of constructing high quality mathematical models for short-term forecasting of macroeconomic processes. The most often approach used for solving the problem is based on regression models though there are also developed competitive probabilistic models that exhibit high forecasting quality in conditions of uncertainties of various kind and nature. Objective. To perform analysis of current economic situation in Ukraine using statistical data; to construct regression models suitable for short-term forecasting of macroeconomic processes selected; to provide a generalized methodology for constructing probabilistic models in the form of Bayesian networks and to construct appropriate network models for macroeconomic processes; to perform necessary computational experiments aiming to model parameter estimation and compare quality of generated forecasts. Methods. To solve the problems stated two basic approaches to construct mathematical models are hired: regression analysis and Bayesian networks constructing using statistical data and expert estimates. A generalized multistep methodology is developed for Bayesian belief networks constructing that uses statistical data and other possible prior information. Results. The models resulted from regression analysis of actual data provide a possibility for generating short-term forecasts of GDP though not always of high quality. Another model was constructed in the form of a Bayesian network. The model turned out to be better than the multiple regression, it provides quite good estimates for probabilities of GDP growth direction. Conclusions. It was shown that application of regression models for describing macroeconomic processes of economy in transition not always finalizes with positive results. This can be explained by numerous out-of-market events (factors) that influence development of the economy. The short-term forecasting results obtained in this case are not always of high quality though quite acceptable. On the other hand probabilistic models such as Bayesian networks provide a possibility for obtaining well substantiated probabilistic estimates for the direction of GDP growth in Ukraine. A substantial advantage of the simple heuristic method used for constructing BN is in its transparency and small number of computing operations.
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 heteroskedastic 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.
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