Forecasting on Global Dynamics for Coronavirus (COVID-19) Outbreak Using Time Series Modelling

ABSTRACT

The spreading and Development of COVID-19 have analyzed which was first officially reported in Wuhan City, in December 2019. Firstly the data have explored in terms of information and quality and after that, the data have cleaned and gone through with feature engineering. Analyzed different types of machine learning-based prediction methods, namely Linear Regression, ARIMA, and SARIMA on the spread of COVID-19 in different regions all over the world. In the end, It has been concluded with the best machine learning model among them for COVID-19 spread forecasting based on theoretical and results in analysis. And also we have discussed that how deep learning can be considered with data limit problem in order to improve the result more dynamically with combination and comparisons of state-of-art approaches for time series problems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.