@article {62625,
	title = {A Comparative Study between ARIMA Model, Holt-Winters\&ndash;No Seasonal and Fuzzy Time Series for New Cases of COVID-19 in Algeria},
	journal = {American Journal of Public Health},
	volume = {9},
	number = {6},
	year = {2021},
	month = {2021},
	pages = {248-256},
	abstract = {
	Background:\&nbsp;Coronavirus disease has become a worldwide threat affecting almost every country in the world. The spread of the virus is likely to continue unabated. The aim of this study is to compare between Autoregressive Integrated Moving Average (ARIMA) model, Fuzzy time series and Holt-Winters {\textendash} No seasonal for forecasting the COVID-19 new cases in Algeria.\&nbsp;



	Methods:\&nbsp;Three different models to predict the number of Covid-19 new cases in Algeria were used. The number of new cases of COVID-19 in Algeria during the period from 24th\&nbsp;February 2020 to 31th July 2021 was modeled according to ARIMA(4,1,2) model, Five based Fuzzy time series models including the Chen model, Heuristic Huareng model, Singh model, Abbasov-Manedova model and NFTS model, and Holt-Winters {\textendash} No seasonal.\&nbsp;



	Results:\&nbsp;The predictive values were obtained from the 1st\&nbsp;August 2021 to 31th December 2021. According to a set of criteria (ME, MAE, MSE, RMSE, U), we found that the FTNS model is the most accurate and best generating model for the values of the number of new cases of Covid-19.\&nbsp;



	Conclusion:\&nbsp;To the best of our knowledge, this is the first comparative study of three models of forecasting of Covid-19 new cases in Algeria. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria. Moreover, this forecast will help the Health authorities to be better prepared to fight the epidemic by engaging their healthcare facilities.

},
	url = {http://article.scipublichealthresearch.com/pdf/ajphr-9-6-4.pdf},
	author = {Hadjira, Abdelmounaim and Salhi, Hicham and El Hafa, Fadoua}
}
