Machine Learning Techniques to Analyze Pandemic-Induced Economic Outliers
9th International Conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2021
; 267:429-439, 2022.
Article
in English
| Scopus | ID: covidwho-1844314
ABSTRACT
Outliers, or outlying observations, are values in data, which appear unusual. It is quite essential to analyze various unexpected events or anomalies in economic domain like sudden crash of stock market, mismatch between country’s per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest to find the insights for the benefit of humankind. These situations can arise due to several reasons, out of which pandemic is a major one. The present COVID-19 pandemic also disrupted the global economy largely as various countries faced various types of difficulties. This motivates the present researchers to identify a few such difficult areas in economic domain, arises due to the pandemic situation and identify the countries, which are affected most under each bucket. Two well-known machine-learning techniques DBSCAN (density based clustering approach) and Z-score (statistical technique) are utilized in this analysis. The results can be used as suggestive measures to the administrative bodies, which show the effectiveness of the study. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
COVID-19 pandemic; Economic outlier; Gross domestic product (GDP) per capita; Human development index (HDI); Machine learning; Economics; Learning algorithms; Gross domestic product per caput; Gross domestic products; Human development index; Machine learning techniques; Outlying observation; Per capita; Unexpected events; Statistics
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
9th International Conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2021
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS