Прогнозування індикаторів сталого розвитку на основі інструментів цифровізації
Ескіз недоступний
Дата
2024
Назва журналу
Номер ISSN
Назва тому
Видавець
Анотація
Сучасні економічні процеси України зазнають впливу війни, інфляції, логістичних труднощів, екологічних проблем, соціальної нерівності, депресії промислових регіонів та масової еміграції. Міграція призводить до втрати людського капіталу, дефіциту робочої сили, змін у структурі населення та скорочення ВРП у постраждалих регіонах. Для сталого розвитку важлив збереження людського капіталу та цифровізація. Основні підходи до прогнозування – класичні (ARIMA, GARCH) і методи інтелектуального аналізу (ANN, SVM, kNN, Random Forest). Для їх реалізації використовуються RStudio, MATLAB, Excel, Google Colab, Kaggle Notebooks. Дослідження 2018–2023 років засвідчують зростання інтересу до цифровізації. Аналіз показників Запорізького регіону (населення, інфраструктура, промисловість, ВРП) демонструє ста- більність за GARCH, чутливість до коливань за Random Forest і консервативність за ARIMA. Вибір моделі залежить від цілей: ARIMA – для стабільних прогнозів, Random Forest і GARCH – для динамічних змін. Хмарні платформи спрощують процес аналізу, сприяючи сталому розвитку регіонів України.
In today's conditions, the economic processes in Ukraine are significantly influenced by numerous factors, including the military situation, inflation, logistical difficulties, environmental problems, social inequality, the depression of industrial regions, and mass emigration. Migration processes, particularly those caused by the war, have had a particularly profound impact, leading to a decrease in human capital, labor shortages, demographic changes, and a reduction in the gross regional product in the affected regions. To ensure sustainable development, it is crucial to focus on preserving human capital and implementing digital technologies. Among the modern methods of time series forecasting, which allow for the analysis of economic indicators in the short- and medium-term, two main approaches are highlighted: classical methods, such as ARIMA and GARCH, and methods of data mining, including neural networks (ANN), support vector machines (SVM), k-nearest neighbors (kNN), and ran- dom forests (Random Forest). These approaches are commonly implemented using programming environments such as RStudio, MATLAB, and Excel with add-ons. However, for more convenient and cost-effective analysis, cloud platforms like Google Colab and Kaggle Notebooks are increasingly used, as they are free and do not require installation. An assessment of sustainable development indicators based on scientific research from 2018–2023, using SciVal and Scopus, shows a growing interest in the digitalization of regional development. Practical analysis of the dynamics of sustainable development in the Zaporizhzhia region revealed indicators such as population size, digital infra- structure development, industrial production volumes, and gross regional product (GRP). Forecasting these indicators using the GARCH, Random Forest, and ARIMA models highlighted their strengths. The GARCH model demonstrated stable growth with a gradual trend. Random Forest showed significant sensitivity to fluctuations, predicting both sharp increases in certain indicators (K5, K6) and declines. ARIMA provided more conservative forecasts, ensuring stability for a number of indicators. The choice of forecasting model depends on the goals: ARIMA is suitable for obtaining stable long- term predictions, while Random Forest and GARCH are more appropriate for accounting for dynamic changes. Cloud platforms significantly simplify this process, making it accessible to a wider range of users. Therefore, the implementation of digital technologies is a key element in ensuring sustainable development in Ukraine's regions.
In today's conditions, the economic processes in Ukraine are significantly influenced by numerous factors, including the military situation, inflation, logistical difficulties, environmental problems, social inequality, the depression of industrial regions, and mass emigration. Migration processes, particularly those caused by the war, have had a particularly profound impact, leading to a decrease in human capital, labor shortages, demographic changes, and a reduction in the gross regional product in the affected regions. To ensure sustainable development, it is crucial to focus on preserving human capital and implementing digital technologies. Among the modern methods of time series forecasting, which allow for the analysis of economic indicators in the short- and medium-term, two main approaches are highlighted: classical methods, such as ARIMA and GARCH, and methods of data mining, including neural networks (ANN), support vector machines (SVM), k-nearest neighbors (kNN), and ran- dom forests (Random Forest). These approaches are commonly implemented using programming environments such as RStudio, MATLAB, and Excel with add-ons. However, for more convenient and cost-effective analysis, cloud platforms like Google Colab and Kaggle Notebooks are increasingly used, as they are free and do not require installation. An assessment of sustainable development indicators based on scientific research from 2018–2023, using SciVal and Scopus, shows a growing interest in the digitalization of regional development. Practical analysis of the dynamics of sustainable development in the Zaporizhzhia region revealed indicators such as population size, digital infra- structure development, industrial production volumes, and gross regional product (GRP). Forecasting these indicators using the GARCH, Random Forest, and ARIMA models highlighted their strengths. The GARCH model demonstrated stable growth with a gradual trend. Random Forest showed significant sensitivity to fluctuations, predicting both sharp increases in certain indicators (K5, K6) and declines. ARIMA provided more conservative forecasts, ensuring stability for a number of indicators. The choice of forecasting model depends on the goals: ARIMA is suitable for obtaining stable long- term predictions, while Random Forest and GARCH are more appropriate for accounting for dynamic changes. Cloud platforms significantly simplify this process, making it accessible to a wider range of users. Therefore, the implementation of digital technologies is a key element in ensuring sustainable development in Ukraine's regions.
Опис
Ключові слова
сталий розвиток, прогнозування часових рядів, GARCH-модель, випадковий ліс, цифрові технології, sustainable development, time series forecasting, GARCH model, Random Forest, digital technologies
Бібліографічний опис
Шапуров О. Прогнозування індикаторів сталого розвитку на основі інструментів цифровізації [Текст] / О. Шапуров, О. Коваленко, В. Стоєв // Цифрова економіка та економічна безпека : науково-практичний журнал / Причорноморський науково-дослідний інститут економіки та інновацій, Сумський державний педагогічний університет імені А. С. Макаренка ; [гол. ред. О. Ю. Кудріна, редкол.: В. В. Божкова, В. І. Борщ, Н. М. Вдовенко та ін.]. – 2024. – № 6 (15). – С. 184–192. – DOI: https://doi.org/10.32782/dees.15-29