  The new Coronavirus (COVID-19) is an emerging disease responsible for
infecting millions of people since the first notification until nowadays.
Developing efficient short-term forecasting models allow knowing the number of
future cases. In this context, it is possible to develop strategic planning in
the public health system to avoid deaths. In this paper, autoregressive
integrated moving average (ARIMA), cubist (CUBIST), random forest (RF), ridge
regression (RIDGE), support vector regression (SVR), and stacking-ensemble
learning are evaluated in the task of time series forecasting with one, three,
and six-days ahead the COVID-19 cumulative confirmed cases in ten Brazilian
states with a high daily incidence. In the stacking learning approach, the
cubist, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian
process (GP) as meta-learner. The models' effectiveness is evaluated based on
the improvement index, mean absolute error, and symmetric mean absolute
percentage error criteria. In most of the cases, the SVR and stacking ensemble
learning reach a better performance regarding adopted criteria than compared
models. In general, the developed models can generate accurate forecasting,
achieving errors in a range of 0.87% - 3.51%, 1.02% - 5.63%, and 0.95% - 6.90%
in one, three, and six-days-ahead, respectively. The ranking of models in all
scenarios is SVR, stacking ensemble learning, ARIMA, CUBIST, RIDGE, and RF
models. The use of evaluated models is recommended to forecasting and monitor
the ongoing growth of COVID-19 cases, once these models can assist the managers
in the decision-making support systems.
