id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-331888-lbtuvdv3	de Souza, Dalton Garcia Borges	Forecasting COVID-19 cases at the Amazon region: a comparison of classical and machine learning models	2020-10-09		.txt	text/plain	2434	178	61	title: Forecasting COVID-19 cases at the Amazon region: a comparison of classical and machine learning models We compare the models autoregressive integrated moving average (ARIMA), Holt-Winters, support vector regression (SVR), k-nearest neighbors regressor (KNN), random trees regressor (RTR), seasonal linear regression with change-points (Prophet), and simple logistic regression (SLR). We evaluate the models according to their capacity to forecast in different historical scenarios of the COVID-19 progression, such as exponential increases, sudden decreases, and stability periods of daily cases. Holt-Winters, support vector regression (SVR), k-nearest neighbors regressor (KNN), 43 random trees regressor (RT), seasonal linear regression with change-points (SLiR) and 44 simple logistic regression (SLR), which dictates the baseline performance in this study. Thus, in this paper, we compared classical and machine learning models to forecast 231 the evolution of COVID-19 in the state. Application of ARIMA and Holt-Winters forecasting model to predict 294 the spreading of COVID-19 for India and its states	./cache/cord-331888-lbtuvdv3.txt	./txt/cord-331888-lbtuvdv3.txt