Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia.

ABSTRACT: COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic's path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, sim

SUBMITTER: Al-Turaiki I 

PROVIDER: S-EPMC8393561 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature