SARIMA-LSTM COMBINATION FOR COVID-19 CASE MODELING

Abstract

The study of SARIMA method in combination with LSTM is interesting to do. This combination method can be convincing and significant because the data collected is numerical and saved based on time. In addition, the proposed method can anticipate datasets, either linear or non-linear. Based on several previous studies, the SARIMA method has the advantage of completing linear datasets while the LSTM method excels in achieving non-linear datasets. Also, both methods have been shown to have an accuracy value compared to some other methods. This study tried to combine the two through several stages of the first stage of applying the SARIMA method using fit datasets (linear data) then residual Dataset (non-linear data) analysed using the LSTM method. The result of the combination methods will be checked for the accuracy value. This research will be compared by using SARIMA and LSTM methods separately. The Dataset used as a trial is COVID-19 patient data in the United States. The results showed that the combination of SARIMA-LSTM method is better than either SARIMA or LSTM alone with RMSE of 0.33905765 and MAE of 0.29077017