In this master’s degree dissertation, it will be discussed how the predictive models
ARIMA, LSTM, MLP and Exponential Smoothin were developed and implemented to
predict time series of confirmed cases and deaths from COVID-19, to assess which
among these obtains the best result. COVID-19 is a disease caused by the coronavirus
called SARS-CoV-2, which has resulted in a large number of infected people globally.
According to the WHO, more than 305 million people are estimated to be infected
worldwide. As it was necessary to use reliable data to carry out the predictions, the
database used for the development of this dissertation is in the public domain and was
provided by the Johns Hopkins University. Time series data of confirmed cases and
deaths from Brazil, India, Italy and the United States of America were compared and
selected to make predictions. About the predict models, the Long Short-Term Memory
neural network is capable of learning long sequences of observations to make
predictions. Besides this, the Multi-Layer Perceptron is a neural network with one or
more hidden layers with an undetermined number of neurons. In addition, the ARIMA
is an autoregressive integrated moving average. Finally, Exponential Smoothing is a
highly accurate prediction model for smoothing time series data. Therefore, after
carrying out the training and testing of each of the models, the performance evaluation
was carried out with the root-mean-square error (RMSE) method and based on the
results of the implemented models for the prediction of data referring to the confirmed
cases and deaths from the COVID-19 pandemic, it was possible to evaluate that the
ARIMA model had the best performance among the others.
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