Voting Regression Model for Covid-19 Time Series Data Analysis

The cases of Coronavirus Disease 2019 are increased at a quick rate in mid-January and the virus is transmitted beyond the borders of China. This disease is spread over 188 countries all around the world. The ARIMA model is an extensively utilized time series model that is constructed for economics applications. This model is adopted and becomes famous due to its statistical properties. The covid-19 time series data analysis is a combination of three tasks such as data acquisition, feature extraction, and classification. In this research work, voting regression is applied for the time-series data analysis. The voting regression is the combination of the elastic net, linear regression, and lasso. The planned paradigm is implemented in python and output is analyzed in terms of MSE and RMSE. The proposed model shows that MSE and RMSE values are low as compared to other models.