@Article{cmc.2020.011335,
AUTHOR = {Ramazan Ünlü, Ersin Namlı},
TITLE = {Machine Learning and Classical Forecasting Methods Based  Decision Support Systems for COVID-19},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1383--1399},
URL = {http://www.techscience.com/cmc/v64n3/39435},
ISSN = {1546-2226},
ABSTRACT = {From late 2019 to the present day, the coronavirus outbreak tragically affected 
the whole world and killed tens of thousands of people. Many countries have taken very 
stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) 
and are still being implemented. In this study, various machine learning techniques are
implemented to predict possible confirmed cases and mortality numbers for the future. 
According to these models, we have tried to shed light on the future in terms of possible 
measures to be taken or updating the current measures. Support Vector Machines (SVM), 
Holt-Winters, Prophet, and Long-Short Term Memory (LSTM) forecasting models are 
applied to the novel COVID-19 dataset. According to the results, the Prophet model gives
the lowest Root Mean Squared Error (RMSE) score compared to the other three models.
Besides, according to this model, a projection for the future COVID-19 predictions of 
Turkey has been drawn and aimed to shape the current measures against the coronavirus.},
DOI = {10.32604/cmc.2020.011335}
}