Using meta-learning to recommend an appropriate time-series forecasting model.

Background: There are various forecasting algorithms available for univariate time series, ranging from simple to sophisticated and computational. In practice, selecting the most appropriate algorithm can be difficult, because there are too many algorithms. Although expert knowledge is required to make an informed decision, sometimes it is not feasible due to the lack of such resources as time, money, and manpower.

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods.

Background: The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence.

Methods: In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran.