SYNTHESIS OF ECONOMETRIC AND NEURAL NETWORK MODELS FOR INDICATORS PREDICTION IN RESEARCH AND INNOVATION IN THE RUSSIAN FEDERATION
The article considers general methodology and architecture of hybrid system models for prediction of economic indicators and its implementation in the form of an integrated information system on the example of research and innovation indicators of the Russian economy. The scheme of the distributedin...
| Main Authors: | , |
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| Format: | Article |
| Language: | Russian |
| Published: |
Government of the Russian Federation, Financial University
2016-06-01
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| Series: | Управленческие науки |
| Subjects: | |
| Online Access: | https://managementscience.fa.ru/jour/article/view/57 |
| _version_ | 1797877465198100480 |
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| author | I. Kolmakov M. Domozhakov |
| author_facet | I. Kolmakov M. Domozhakov |
| author_sort | I. Kolmakov |
| collection | DOAJ |
| description | The article considers general methodology and architecture of hybrid system models for prediction of economic indicators and its implementation in the form of an integrated information system on the example of research and innovation indicators of the Russian economy. The scheme of the distributedinformation-analytical system is demonstrated. The general verification process algorithm of the modelprediction unit is presented, which contributes significantly to the credibility of the forecast results. The object of the study is a unified system of hybrid models, combining econometric and neural network modelsinto a single system of hybrid economic models. The structure of the hybrid forecasting system consists of two subsystems: the subsystem of the distributed econometric forecast models and subsystem of the distributed neural network prediction models. The objective reasons, under which the level best of regression modelsis reached, are identified. The subsystem architecture of the distributed neural network models developedin the programming language Python with the use of the web framework Django is described. The stages of indicators forecasting in a hybrid model are shown. The hybrid models functional structure based on the use of software modules are considered. The use of such a system allows not only to improve the accuracy and quality of the forecasts, but also to apply them in the control loop foreaching the targets. |
| first_indexed | 2024-04-10T02:18:41Z |
| format | Article |
| id | doaj.art-970c100e138d45d5b0984adf3af38a81 |
| institution | Directory Open Access Journal |
| issn | 2304-022X 2618-9941 |
| language | Russian |
| last_indexed | 2024-04-10T02:18:41Z |
| publishDate | 2016-06-01 |
| publisher | Government of the Russian Federation, Financial University |
| record_format | Article |
| series | Управленческие науки |
| spelling | doaj.art-970c100e138d45d5b0984adf3af38a812023-03-13T08:28:53ZrusGovernment of the Russian Federation, Financial UniversityУправленческие науки2304-022X2618-99412016-06-0162273710.26794/2304-022X-2016--2-27-3757SYNTHESIS OF ECONOMETRIC AND NEURAL NETWORK MODELS FOR INDICATORS PREDICTION IN RESEARCH AND INNOVATION IN THE RUSSIAN FEDERATIONI. Kolmakov0M. Domozhakov1Российский экономический университет им. Г.В. ПлехановаРоссийский экономический университет им. Г.В. ПлехановаThe article considers general methodology and architecture of hybrid system models for prediction of economic indicators and its implementation in the form of an integrated information system on the example of research and innovation indicators of the Russian economy. The scheme of the distributedinformation-analytical system is demonstrated. The general verification process algorithm of the modelprediction unit is presented, which contributes significantly to the credibility of the forecast results. The object of the study is a unified system of hybrid models, combining econometric and neural network modelsinto a single system of hybrid economic models. The structure of the hybrid forecasting system consists of two subsystems: the subsystem of the distributed econometric forecast models and subsystem of the distributed neural network prediction models. The objective reasons, under which the level best of regression modelsis reached, are identified. The subsystem architecture of the distributed neural network models developedin the programming language Python with the use of the web framework Django is described. The stages of indicators forecasting in a hybrid model are shown. The hybrid models functional structure based on the use of software modules are considered. The use of such a system allows not only to improve the accuracy and quality of the forecasts, but also to apply them in the control loop foreaching the targets.https://managementscience.fa.ru/jour/article/view/57сфера исследований и инновацийсистемы регрессионных уравнениймодели краткосрочного прогнозаверификация прогнозасистема нейросетевых моделейсистема гибридных моделей |
| spellingShingle | I. Kolmakov M. Domozhakov SYNTHESIS OF ECONOMETRIC AND NEURAL NETWORK MODELS FOR INDICATORS PREDICTION IN RESEARCH AND INNOVATION IN THE RUSSIAN FEDERATION Управленческие науки сфера исследований и инноваций системы регрессионных уравнений модели краткосрочного прогноза верификация прогноза система нейросетевых моделей система гибридных моделей |
| title | SYNTHESIS OF ECONOMETRIC AND NEURAL NETWORK MODELS FOR INDICATORS PREDICTION IN RESEARCH AND INNOVATION IN THE RUSSIAN FEDERATION |
| title_full | SYNTHESIS OF ECONOMETRIC AND NEURAL NETWORK MODELS FOR INDICATORS PREDICTION IN RESEARCH AND INNOVATION IN THE RUSSIAN FEDERATION |
| title_fullStr | SYNTHESIS OF ECONOMETRIC AND NEURAL NETWORK MODELS FOR INDICATORS PREDICTION IN RESEARCH AND INNOVATION IN THE RUSSIAN FEDERATION |
| title_full_unstemmed | SYNTHESIS OF ECONOMETRIC AND NEURAL NETWORK MODELS FOR INDICATORS PREDICTION IN RESEARCH AND INNOVATION IN THE RUSSIAN FEDERATION |
| title_short | SYNTHESIS OF ECONOMETRIC AND NEURAL NETWORK MODELS FOR INDICATORS PREDICTION IN RESEARCH AND INNOVATION IN THE RUSSIAN FEDERATION |
| title_sort | synthesis of econometric and neural network models for indicators prediction in research and innovation in the russian federation |
| topic | сфера исследований и инноваций системы регрессионных уравнений модели краткосрочного прогноза верификация прогноза система нейросетевых моделей система гибридных моделей |
| url | https://managementscience.fa.ru/jour/article/view/57 |
| work_keys_str_mv | AT ikolmakov synthesisofeconometricandneuralnetworkmodelsforindicatorspredictioninresearchandinnovationintherussianfederation AT mdomozhakov synthesisofeconometricandneuralnetworkmodelsforindicatorspredictioninresearchandinnovationintherussianfederation |