The mortality modeling of covid-19 patients using a combined time series model and evolutionary algorithm

(1) * Imam Tahyudin Mail (Universitas Amikom Purwokerto, Indonesia)
(2) Rizki Wahyudi Mail (Universitas Amikom Purwokerto, Indonesia)
(3) Wiga Maulana Mail (Universitas Amikom Purwokerto, Indonesia)
(4) Hidetaka Nambo Mail (Artificial Intelligence Laboratory, Kanazawa University, Japan)
*corresponding author


COVID-19 pandemics for as long as two years ago since 2019 gives many insights into various aspects, including scientific development. One of them is the fundamental research of computer science. This research aimed to construct the best model of COVID-19 patients’ mortality and obtain less prediction errors. We performed the combination methods of time series, SARIMA, and Evolutionary algorithm, PARCD, to predict male patients who died because of COVID-19 in the USA, containing 1.008 data. So, this research proposed that SARIMA-PARCD has a powerful combination for addressing the complex problem in a dataset. The prediction error of SARIMA-PARCD was compared with other methods, i.e., SARIMA, LSTM, and the combination of SARIMA-LSTM. The result showed that the SARIMA-PARCD has the smallest MSE value of 0.0049. Therefore, the proposed method is competitive to implement in other cases with similar characteristics. This combination is robust for solving linear and non-linear problems.


Time Series Analysis; Evolutionary Algorithm; SARIMA-PARCD; COVID-19 Patients; LSTM



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