Mintodê Nicodème Atchadé: National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Republic of Benin. ORCID
Yves Morel Sokadjo: University of Abomey-Calavi/International Chair in Mathematical Physics and Applications (ICMPA: UNESCO-Chair), Abomey-Calavi , Republic of Benin. ORCID
Aliou Djibril Moussa: National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Republic of Benin.
Svetlana Vladimirovna Kurisheva: Department of Statistics and Econometrics, Saint-Petersburg State University of Economics, Saint-Petersburg , Russia.
Marina Vladimirovna Bochenina: Department of Statistics and Econometrics, Saint-Petersburg State University of Economics, Saint-Petersburg , Russia. ORCID
Many papers have proposed forecasting models and some are accurate and others are not. Due to the debatable quality of collected data about COVID-19, this study aims to compare univariate time series models with cross-validation and different forecast periods to propose the best one. We used the data titled "Coronavirus Pandemic (COVID-19)" from "'Our World in Data" about cases for the period of 31 December 2019 to 21 November 2020. The Mean Absolute Percentage Error (MAPE) is computed per model to make the choice of the best fit. Among the univariate models, Error Trend Season (ETS), Exponential smoothing with multiplicative error-trend, and ARIMA; we got that the best one is ETS with additive error-trend and no season. The findings revealed that with the ETS model, we need at least 100 days to have good forecasts with a MAPE threshold of 5%.
