DECISION SUPPORT SYSTEM ARCHITECTURE FOR FORECASTING OF NONSTATIONARY FINANCIAL PROCESSES AND CORRESPONDING RISKS
Keywords:model, economic and financial processes, statistical criteria, forecasts estimates risks.
AbstractA concept of a decision support system (DSS) for modeling and forecasting of economic and financial processes is proposed as well as its software implementation. The main functions of the DSS are in modeling and short-term forecasting of nonstationary nonlinear time series in various areas of human activities. On the basis of the proposed concept the software system was developed that possesses a set of the following useful features such as constructing of high quality forecasting mathematical models using time series statistical data; testing automatically quality of models and the forecasts based on them using appropriate sets of statistical criteria; combining various computational procedures for improving forecasts estimates; easy modification and expanding of existing system functionality. The software has been created with the use of cross platform instrumentation Qt and programming languages C++ and QML. Thanks to the open system architecture the functional possibilities of the system created can be expanded substantially at the expense of new model types, extra computational techniques and analytical instruments, possibilities for graphical representation of results as well as the means for interaction with user.
Hollsapple, C. W. Decision support systems / C. W. Hollsapple, A. B. Winston. – Saint Paul : West Publishing Company, 1996. – 860 p.
Burstein, F. Handbook of Decision Support Systems / F. Burstein, C. W. Holsapple. – Berlin : Springer-Verlag, 2008. – 908 p.
Sytnyk, V. F. Decision support systems / V. F. Sytnyk. – Kyiv : KNEU, 2004.– 614 p.
Polovcev, O. V. System approach to modeling, forecasting, and control of financial and economic processes / O. V. Polovcev, P. I. Bidyuk, L. O. Korshevnyuk. – Donetsk : Oriental Publishing House, 2009. – 286 p.
Bidyuk, P. I. Computer based decision support systems / P. I. Bidyuk, O. P. Gozhij, L. O. Korshevnyuk. – Mykolaiv : Chornomorsky State University, 2012. – 380 p.
Gupal, A. M. Optimal recognition procedures / A. M. Gupal, I. V. Sergiyenko. – Kyiv: Naukova Dumka, 2008. – 232 p.
Tsay, R. S. Analysis of financial time series / R. S. Tsay. – Hoboken : Wiley & Sons, Inc., 2010. – 715 p. 8. Bidyuk, P. I. Methods of Forecasting / P. I. Bidyuk, O. S. Menyailenko, O. V. Polovcev. – Lugansk : Alma Mater, 2008. – 608 p.
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Copyright (c) 2014 O. A. Kozhukhivska, A. O. Fefelov, P. I. Bidyuk, A. D. Kozhukhivskyi
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