| Abstrakt: |
The methods currently used for detecting situational deformations in the time series of economic indicators have a number of major flaws, which encourages further research and development in this field aimed at improving the quality and stability of the regression models in the context of digital transformations. The purpose of this research is to devise a methodology for predicting economic indicators in the context of situational response to digital transformation. We examine methods for predicting economic indicators through time series analysis, following which the vector of proactive development can be determined. In order to achieve our objectives, we employed various methods, including mathematical statistics, mathematical modeling, numerical methods, and regression analysis. Our analysis of seasonality in the time series of economic indicators, given the cyclic dominance and modified series, allows us to conclude the need for their structural decomposition with proactive data rejection and modifications and considering their possible displacement. We devised our own original methodology for better processing of statistical data and improved stability of linear regression models ineffective forecasts in the context of situational response of digital transformations. The statistical tools we suggest are likely to enhance the quality of the economic forecasts obtained with the use of regression models (from 15%) due to the preliminary processing of source data and determination of the cyclic dominance of the modified series. The study shows that the methodology for predicting time series in the context of situational response offers the fastest and most accurate data analysis. [ABSTRACT FROM AUTHOR] |
