The Special Aspects of Devising a Methodology for Predicting Economic Indicators in the Context of Situational Response to Digital Transformation
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 digita...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2023-12-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6839 |
| Summary: | 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. |
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| ISSN: | 2086-9614 2087-2100 |