Published November 16, 2023 | Version v1
Journal article Open

Probabilistic forecast model as a time management tool

  • 1. Department of Management and Administration, V. N. Karazin Kharkiv National University, Ukraine
  • 2. Department of Management, Business and Professional Communications, V. N. Karazin Kharkiv National University, Ukraine
  • 3. Department of Media Systems and Technology, Kharkiv National University of Radio Electronics, Ukraine

Description

Forecasting plays an important role in economic research. This allows you to justify and make informed and effective decisions. For these purposes, various methods, approaches and models can be used. At the same time, among the possible models, we highlight probabilistic models that allow us to take into account individual characteristics of the processes, phenomena, and objects under study. We can also build models with given characteristics, which allow us to plan some developments. At the same time, the process of predictive modeling is important in the allocation of various resources. Here, a special place is occupied by the resource of time, which allows one to effectively influence the redistribution of other resources. Thus, we consider probabilistic forecasting models as a time management tool. Using the example of specific probabilistic characteristics, we justify the construction of a certain probabilistic forecast model. We show the possibility of using such a model in time management. The work also provides a number of diagrams and graphs that allow you to understand the progress of this study.

Files

139-146 Anatoliy Babichev.pdf

Files (898.2 kB)

Name Size Download all
md5:b8b7818466374e26e83c81d91768f682
898.2 kB Preview Download

Additional details

References

  • 1. De Bruijn, E. J., & Antonides, G. (2022). Poverty and economic decision making: a review of scarcity theory. Theory and Decision, 92(1), 5-37.
  • 2. Png, I. (2022). Managerial economics. Routledge.
  • 3. Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and economic development of economy, 17(2), 397-427.
  • 4. Азаренкова, Г., & Ляшенко, В. (2009). Відношення переваг у порівняльній оцінці діяльності банків. Банківська справа, 5, 65-72.
  • 5. Ahmad, M. A., & et al.. (2019). Computational complexity of the accessory function setting mechanism in fuzzy intellectual systems. International Journal of Advanced Trends in Computer Science and Engineering, 8(5), 2370-2377.
  • 6. Kuzemin, O., & Lyashenko, V. Microsituation Concept in GMES Decision Support Systems. Intelligent Data Processing in Global Monitoring for Environment and Security (pр. 217–238). – 2011. – Р. 217-238.
  • 7. Слюніна, Т. Л., Бережний, Є. Б., & Ляшенко, В. В. (2007). Розвиток вітчизняної мережі банківських установ: особливості та регіональні аспекти. Вісник ХНУ ім. В. Н. Каразіна. Економічна серія, 755. 84–88.
  • 8. Lyashenko, V. (2014). Efficiency of bank crediting of real sector of economy in the context of separate banking groups: an empirical example from Ukraine. International Journal of Accounting and Economics Studies, 2(2), 74-79.
  • 9. Куштим, В. В., & Ляшенко, В. В. (2007). Динаміка розвитку банківського сегмента міжнародного фінансового ринку. Фінанси України, 12, 96-105.
  • 10. Ляшенко В. В. (2007). Интерпретация и анализ статистических данных, описывающих процессы экономической динамики. Бизнес Информ, 9(2), 108-113.