چکیده مقاله
The rising trend of COVID 19 has put the world economy and the healthcare system of countries over the world in the crisis At present, the shortage of healthcare facilities has been emerging as an urgent challenge Designing strategic and operational decisions for hedging against the risk of this epidemic requires an estimate of the new cases of the infection in the future This paper presents different forecasting methods to predict the time series of COVID 19 in Iran To forecast the daily and cumulative fluctuations of new cases, several Holt Winters exponential smoothing methods are used These methods include the additive Holt Winters method, the extended Holt Winters method, and the additive Holt Winters method with damped trend The methods are tested on the data series of Iran Different efficiency measures are also presented to evaluate the performance of forecasting methods The empirical results show that the extended Holt Winters method leads to more accurate forecasts Moreover, the forecasted values obtained with other methods are reliable and satisfactory, especially in forecasting the cumulative cases of COVID 19
کلیدواژهها
نویسندگان
ناعمه زرین پور
Department of Industrial Engineering, Shiraz University of Technology, Shiraz, Iran
Neda Khosravi Fard
Department of Industrial Engineering, Shiraz University of Technology, Shiraz, Iran
شیوه ارجاع
Zarrinpoor, Naeme and Khosravi Fard, Neda,1404,Time series forecasting of COVID-19 in Iran using Holt-Winters smoothing methods,11th International Conference on Industrial and System Engineering,Mashhad
ارائهشده در
مجموعه مقالات یازدهمین کنفرانس بین المللی مهندسی صنایع و سیستم ها2 مهر 1404 · مشهد