id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-325321-37kyd8ak	Iftikhar, H.	Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study	2020-09-22		.txt	text/plain	2618	144	57	title: Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study In this work, we used five different univariate time series models including; Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Nonparametric Autoregressive (NPAR) and Simple Exponential Smoothing (SES) models for forecasting confirmed, death and recovered cases. The findings show that the time series models are useful in predicting COVID-19 confirmed, deaths and recovered cases. In this work, the COVID-19 confirmed, deaths and recovered counts times series are plotted in Figure 1 (left-column) daily and Figure 1 (right-column) cumulative cases. The main purpose of this work was to forecast confirmed, deaths and recovered cases of COVID-19 for Pakistan using five different univariate time series models including; Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Nonparametric Autoregressive (NPAR) and Simple exponential smoothing (SES) models.	./cache/cord-325321-37kyd8ak.txt	./txt/cord-325321-37kyd8ak.txt