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2020 ; 138
(ä): 110015
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Comparative analysis and forecasting of COVID-19 cases in various European
countries with ARIMA, NARNN and LSTM approaches
#MMPMID32565625
K?rba? ?
; Sözen A
; Tuncer AD
; Kazanc?o?lu F?
Chaos Solitons Fractals
2020[Sep]; 138
(ä): 110015
PMID32565625
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In this study, confirmed COVID-19 cases of Denmark, Belgium, Germany, France,
United Kingdom, Finland, Switzerland and Turkey were modeled with Auto-Regressive
Integrated Moving Average (ARIMA), Nonlinear Autoregression Neural Network
(NARNN) and Long-Short Term Memory (LSTM) approaches. Six model performance
metric were used to select the most accurate model (MSE, PSNR, RMSE, NRMSE, MAPE
and SMAPE). According to the results of the first step of the study, LSTM was
found the most accurate model. In the second stage of the study, LSTM model was
provided to make predictions in a 14-day perspective that is yet to be known.
Results of the second step of the study shows that the total cumulative case
increase rate is expected to decrease slightly in many countries.