http://dspace.nbuv.gov.ua:80/handle/123456789/111462 2026-04-08T12:50:56Z 2026-04-08T12:50:56Z http://dspace.nbuv.gov.ua:80/handle/123456789/113746 2017-02-14T01:02:42Z 2016-01-01T00:00:00Z

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2016-01-01T00:00:00Z http://dspace.nbuv.gov.ua:80/handle/123456789/113392 2017-02-08T01:03:14Z 2016-01-01T00:00:00Z

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2016-01-01T00:00:00Z Савченко, Е.А. Тутова, Е.В. http://dspace.nbuv.gov.ua:80/handle/123456789/113391 2017-02-08T01:02:52Z 2016-01-01T00:00:00Z

Методика анализа данных, моделирования и прогнозирования уровня развития человеческого потенциала стран мира и регионов Украины Савченко, Е.А.; Тутова, Е.В. Описана методика применения корреляционно-регрессионного анализа и комбинаторного алгоритма МГУА для анализа данных, моделирования и прогнозирования уровня человеческого развития стран мира и регионов Украины. Приведены примеры решения задач восстановления данных, отбора информативных аргументов, построения моделей для анализа и прогноза.; Описано методику застосування кореляційно-регресійного аналізу та комбінаторного алгоритму МГУА для аналізу даних, моделювання і прогнозування рівня людського розвитку країн світу та регіонів України. Подано приклади розв'язання задач відновлення даних, відбору інформативних аргументів, побудови моделей для аналізу і прогнозу.; Introduction. In spite of existing techniques, a problem of data analysis, modeling, and forecasting of social and economic processes is of current importance since the macroeconomic data often contain uncertainty. They also can be fuzzy and noisy. Therefore, it is often difficult to find a solution for such problems with the acceptable accuracy. Purpose. The purpose of this article is to develop a procedure to solve the problems of the human development level data analysis, modeling, and forecasting for the countries of the world and the regions of Ukraine based on the combinatorial GMDH algorithm and the correlationregression analysis. This procedure promotes improvement to the decision-making process in the field of the social and economic development policy in Ukraine both on the regional level and to compare the results achieved by our state on the way of progress in the field of the human potential development with other countries. Methods. By the development of this procedure the combinatorial GMDH algorithm and regression analysis are used to build these models. Correlation analysis was applied to analyze the level of influence of indicators on the output quantity and to choose the most informative factors. Results. A procedure for data analysis, modeling and forecasting to assess the human development level in the countries of the world and the Ukrainian regions was elaborated. This procedure can be used of recovering the data, selecting the informative arguments, building a model for the identification and forecasting, and extending the modeling space owing to the new arguments’ addition. Conclusion. The application results of this procedure are recommendations for the possible correction of some aspects of the human development. It gives a possibility to improve the decision making process in the social and economic sphere. The worked out procedure can be used by the specialists in the field of the social and economical development, international relations to analyze and forecast the main trends in the regional development and international policy.

2016-01-01T00:00:00Z Ефименко, С.Н. http://dspace.nbuv.gov.ua:80/handle/123456789/113390 2017-02-08T01:03:21Z 2016-01-01T00:00:00Z

Построение систем прогнозных моделей многомерных взаимосвязанных процессов Ефименко, С.Н. Разработаны теоретические основы рекуррентно-параллельных вычислений в комбинаторном алгоритме МГУА для моделирования и прогнозирования сложных многомерных взаимосвязанных процессов в классе моделей векторной авторегрессии. Продемонстрирована эффективность разработанного алгоритма.; Розроблено теоретичні основи рекурентно-паралельних обчислень у комбінаторному алгоритмі МГУА для моделювання та прогнозування складних багатовимірних взаємозв’язаних процесів у класі моделей векторної авторегресії. Продемонстровано ефективність розробленого алгоритму.; Introduction. The problem of the mathematical modelling and prediction of the multidimensional interrelated time series is considered. It is used in economy, ecology and sociology. While many scientific proceedings are dedicated to modelling of one-dimensional time series, the experience of multidimensional time series modelling is insufficient. Methods. An approach to the structural and parameters identification of the multidimensional time series is considered when parameters for every model is estimated independently. An algorithm with selecting of more than one best model for every process is used. The purpose is to combine all possible variants of system models and to select the best one by additional criterion. Results. Theoretical grounds of recurrent-and-parallel computing in combinatorial GMDH algorithm and software for modeling and prediction of complex multidimensional interrelated processes in the class of vector autoregression models are developed. Conclusion. The scheme of paralleling for recurrent COMBI algorithm allows to solve the problem when arguments amount exceeds capability of scheme with the exhaustive search. The effectiveness of the constructed algorithm is demonstrated by prediction of the interrelated processes in the field of investment activity of Ukraine with the purpose of information support of administrative decisions.

2016-01-01T00:00:00Z