Time Series Modelling and Prediction of the Coronavirus Outbreaks (COVID-19) in the World

Abstract

Coronaviruses are a huge family of viruses that affect neurological, gastrointestinal, hepatic and respiratory systems. The numbers of confirmed cases are increased daily in different countries, especially in Unites State America, Spain, Italy, Germany, China, Iran, South Korea and others. The spread of the COVID-19 has many dangers and needs strict special plans and policies. Therefore, to consider the plans and policies, the predicting and forecasting the future confirmed cases are critical. The time series models are useful to model data that are gathered and indexed by time. Classical time series is based on the assumption that the error terms are symmetric. But there exist many situations in the real world that assumption of symmetric distribution of the error terms is not satisfactory. In our methodology, we consider the time series models based on the two-piece scale mixtures of normal (TP–SMN) distributions. The mentioned class of distributions is a rich class of distributions family that covers the robust symmetric/asymmetric light/heavy tailed distributions. The proposed time series models works well than ordinary Gaussian and symmetry models (especially for COVID-19 datasets), and were fitted initially to the historical COVID-19 datasets. Then, the time series that has the best fit to each of the dataset is selected. Finally, the selected models are used to predict the number of confirmed cases and death rate of COVID-19 in the world. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.