COVID-19: Pushing the Limits of Time Series Big Data
DOI:
https://doi.org/10.33102/uij.vol33noS4.416Keywords:
Big Data, time-series, predictive analytics, data analyticsAbstract
In Malaysia, the pandemic coronavirus disease 2019 (COVID-19) was first detected on 25th January and has been spreading massively and reported to have reached more than 20,000 new cases per day from July to August 2021. COVID-19 data is voluminous describing the pandemic trend around the globe. How does Big Data help decision-makers understand the pandemic behaviour which is very crucial in responding to the situation? How do data analytics on the COVID-19 spreading pattern which is time-series in nature may provide insight into the situation that may lead to a better response through forecasting future trends? This paper aims to explain the concept of Big Data and its applications that demonstrates its potential for responding to the pandemic. COVID-19 data analytic is proposed using sliding window time-series forecasting method and demonstrated using data from 25th January until 10th October 2020 obtained from the Malaysian Ministry of Health and Department of Statistics Malaysia website. The data analytics demonstrated the value gain for useful insights.
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