Abstract
The entire world is undergoing a hard-hitting scenario and trying to combat the COVID-19 by recent technological advancements, which involves machine learning chiefly. The forecasting demand has become a prerequisite as it helps the government officials and other organizations to make well-versed verdicts and impose pertinent measures to benefit the living conditions of the individuals around the globe. Consequently, the current paper focuses on four significant machine learning methods (Facebook Prophet, Auto Regression, Vector Auto Regression, and Holt-Winters), which help forecast the total confirmed and daily confirmed cases. Moreover, the paper reveals the ideal method for the future forecast based on the attained results and efficacy rate. The results of the study reveal the best methods for the considered countries based on the calculation of Error percentage. Out of the four Machine Learning models, AR and FB models stood out as the best methods when compared to the other two.
Highlights
In the current striking scenario, forecasting of COVID-19 cases has become quite essential
The current section focuses on the forecasting of the COVID-19 cases using classical machine learning models for the three countries (i.e., India, US and Oman)
Over the past few months, much insightful work was done on COVID-19, and the optimal data got congregated for the present study from the World Health Organization (WHO) portal
Summary
In the current striking scenario, forecasting of COVID-19 cases has become quite essential. It helps to create awareness for both government and the public due to which scenario-planning tools take place in every other sector. Time Series Forecasting is a prominent area of machine learning and is used for many crucial predictions where time plays a major role. It is known as extrapolation in the classical statistical handling of Time Series data.
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