Cross-Validation Comparison of COVID-19 Forecast Models

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 dat...

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Detalles Bibliográficos
Autores principales: Atchadé, Mintodê Nicodème, Sokadjo, Yves Morel, Moussa, Aliou Djibril, Kurisheva, Svetlana Vladimirovna, Bochenina, Marina Vladimirovna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2021
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150153/
https://www.ncbi.nlm.nih.gov/pubmed/34056624
http://dx.doi.org/10.1007/s42979-021-00699-1
Descripción
Sumario: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%.

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