Видання зареєстровані авторами шляхом самоархівування
Permanent URI for this communityhttps://devessuir.sumdu.edu.ua/handle/123456789/1
Browse
6 results
Search Results
Item Analysis of modern approaches for the prediction of electric energy consumption(De Gruyter, 2020) Kalimoldayev, M.; Дрозденко, Олексій Олександрович; Дрозденко, Алексей Александрович; Drozdenko, Oleksii Oleksandrovych; Коплик, Ігор Володимирович; Коплык, Игорь Владимирович; Koplyk, Ihor Volodymyrovych; Маринич, Тетяна Олександрівна; Маринич, Татьяна Александровна; Marynych, Tetiana Oleksandrivna; Abdildayeva, А.; Zhukabayeva, T.A review of modern methods of forming a mathematical model of power systems and the development of an intelligent information system for monitoring electricity consumption. The main disadvantages and advantages of the existing modeling approaches, as well as their applicability to the energy systems of Ukraine and Kazakhstan, are identified. The main factors that affect the dynamics of energy consumption are identified. A list of the main tasks that need to be implemented in order to develop algorithms for predicting electricity demand for various objects, industries and levels has been developed.Item Econometric modeling of nonstationary processes(Karazin National University, 2015) Lytsenko, M.; Маринич, Тетяна Олександрівна; Маринич, Татьяна Александровна; Marynych, Tetiana OleksandrivnaEconometric research of nonstationary time series on causality, cointegration relation and adequate simulation methods was conducted. VAR and VEC models were found to be the most appropriate ways to make reliable prediction and scenario analysis of macro financial data under unstable economic conditions. These econometric techniques were approbated on the financial indicators of Ukrainian economy.Item Regional Sustainability Assessment Through Multivariate Statistical Analysis(Sumy State University, 2018) Маринич, Тетяна Олександрівна; Маринич, Татьяна Александровна; Marynych, Tetiana Oleksandrivna; Smolenko, S.The work assesses regional di fferentiation based on the extended and classical vari ables’ selection using multi variate statistical techniques: cluster and principal component analysis.Item Features of International Reserves Management Underglobal Financial Turbulance(Международное научное объединение экономистов “Консилиум”, 2013) Маринич, Тетяна Олександрівна; Маринич, Татьяна Александровна; Marynych, Tetiana OleksandrivnaThe importance of the International reserves (IR) management has increased since the Asian financial crisis of 1998. The turbulence of the modern financial world poses the questions of the reserves adequacy, optimal size, structure and currency distribution of the country’s liquid foreign assets.Item Empirical assessment of long-term aspects of sustainable regional development(Institute of Society Transformation, 2017) Маринич, Тетяна Олександрівна; Маринич, Татьяна Александровна; Marynych, Tetiana OleksandrivnaSustainable development becomes one of the biggest challenges of the modern world. Despite the global issue, there is an increasing relevance of the intensification of the regional research and collaboration. The paper summarises the international experience of the sustainable development assessment and modelling. It aims to identify factors that contribute to the regional sustainability and to empirically investigate the causal links and long-run cointegration relationships between them using quarterly time series data from 2001 to 2016 for Sumy region of Ukraine. The methodology for the study is based on the vector autoregressive (VAR) and the Johansen VEC approach procedure. The results of the estimation of the aggregate Index of sustainable development (ISD) show a great impact and variability of its economic component, which influences social and environmental indicators as well. The findings of econometric modelling have revealed long-term relationships and positively significant effects of capital related to the labour ratio and education on the regional economic growth. The lack of macroeconomic stability and unrealised potential of technological progress are deemed to make a negative impact on sustainable development. Further considerations based on the intersectoral econometric analysis are provided to support the appropriate policy making suggestions.Item Comparative analysis of univariate time series modeling and forecasting techniques for short-term unstable data(Національний технічний університет «Харківський політехнічний інститут», 2017) Маринич, Тетяна Олександрівна; Маринич, Татьяна Александровна; Marynych, Tetiana Oleksandrivna; Назаренко, Людмила Дмитрівна; Назаренко, Людмила Дмитриевна; Nazarenko, Liudmyla Dmytrivna; Хоменко, Наталія Григорівна; Хоменко, Наталия Григорьевна; Khomenko, Nataliia HryhorivnaThe article summarizes the international experience in univariate time series modeling approaches and methodology. It aims to make empirical assessment of their relevance and forecasting power for short sample volatile data with numerous aberrant observations and structural breaks with the help of the time series R packages. The findings revealed the pitfalls of outliers’ neglection including stationarity and model misspecification, biased parameter estimates, deterioration of residuals’ properties and prediction accuracy of the models. Empirical research demonstrated the outperformance of the outlier detection methods versus robust approaches that use smaller weights for aberrant observations. We tested a method of improving the forecasting power of the ARMA models by proper identification of hidden patterns and incorporation of additional information about extraordinary events into the model. We also considered frequency domain and nonparametric methods including exponential smoothing, seasonal and trend-cycle decomposition, structural and neural networks models to make comparative forecasting diagnostics. The findings showed slightly worse accuracy of the exponential smoothing and structural state-space models for short prediction horizons and their outperformance for longer forecasting periods. Neural networks showed outstanding in-sample approximation but poor out-of-sample quality. We recommend further studying of the Bayesian regime switching models that have proven to be a comprehensive way to explore hidden patterns in data, as well as dynamic factor multivariate models that can improve explanatory and forecasting power of the time series models in various applications.