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.
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References
Vitenu-Sackey, P.A.: The impact of Covid-19 pandemic on the global economy: emphasis on poverty alleviation and economic growth. Mendeley Data V1,(2020). https://doi.org/10.17632/b2wvnbnpj9.1
OECD (2020) Unemployment rate (indicator). https://doi.org/10.1787/52570002-en. Accessed 07 Dec 2020
Polyakova, M., Kocks, G., Udalova, V., Finkelstein, A.: Initial economic damage from the COVID-19 pandemic in the United States is more widespread across ages and geographies than initial mortality impacts. Proc. Natl. Acad. Sci. 117(45), 27934–27939 (2020)
Stojkoski, V., Utkovski, Z., Jolakoski, P., Tevdovski, D., & Kocarev, L.: The socio-economic determinants of the coronavirus disease (COVID-19) pandemic (2020). arXiv:2004.07947
Chetty, R., Friedman, J., Hendren, N., Stepner, M.: The economic impacts of COVID-19: Evidence from a new public database built from private sector data. Opportunity Insights (2020)
Mele, M., Magazzino, C.: Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence. Environ. Sci. Pollut. Res. 1–9 (2020)
Bonaccorsi, G., Pierri, F., Cinelli, M., Porcelli, F., Galeazzi, A., Flori, A., Pammolli, F.: Evidence of economic segregation from mobility lockdown during covid-19 epidemic. (2020) Available at SSRN 3573609
Beyer, R. C., Franco-Bedoya, S., Galdo, V.: Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity. World Dev. 105287 (2020)
Albulescu, C.T.: COVID-19 and the United States financial markets’ volatility. Finance Res. Lett. 38, 101699 (2021)
Behera, S., Rani, R.: Comparative analysis of density based outlier detection techniques on breast cancer data using Hadoop and MapReduce. In: International Conference on Inventive Computation Technologies (ICICT), (vol. 2, pp. 1–4). IEEE (2016)
Berkhin, P.: ‘A survey of clustering data mining techniques’, Grouping multidimensional data, pp. 25–71. Springer, Berlin, Heidelberg (2006)
Kannan, K.S., Manoj, K., Arumugam, S.: Labeling methods for identifying outliers. Int. J. Stat. Syst. 10(2), 231–238 (2015)
Accessed Google Colab from https://colab.research.google.com/notebooks/intro.ipynb
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Desarkar, A., Das, A. (2022). Machine Learning Techniques to Analyze Pandemic-Induced Economic Outliers. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_42
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DOI: https://doi.org/10.1007/978-981-16-6616-2_42
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