使用长短期记忆网络（LSTM）对印度尼西亚新冠肺炎确诊病例进行预测。

Forecasting of Covid-19 positive cases in Indonesia using long short-term memory (LSTM).

作者信息

Sunjaya Bryan Alfason, Permai Syarifah Diana, Gunawan Alexander Agung Santoso

机构信息

Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.

Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.

出版信息

Procedia Comput Sci. 2023;216:177-185. doi: 10.1016/j.procs.2022.12.125. Epub 2023 Jan 10.

DOI:10.1016/j.procs.2022.12.125

PMID:36643183

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9829418/

Abstract

Since the emergence of Covid-19, the condition of Covid-19 has increased and decreased several times along with the emergence of new variants. Therefore, change occurs quickly and is extreme. If the positive cases of covid occur beyond medical capacity, there will be inequality. Therefore, it is important to predict the number of positive cases of covid to avoid this. The objective of this research is to predict the number of positive cases of Covid-19 in Indonesia using the ARIMA and LSTM methods. The two methods were compared to obtain the best method for predicting positive cases of Covid-19 in Indonesia. The data used in this research is the number of positive cases of Covid-19 in Indonesia from 2020 to 2022. Based on the results of ARIMA modeling, showed that the prediction results for the number of positive Covid -19 cases are still not good. This is because the ARIMA model produced does not meet the assumptions. Therefore, modeling was carried out using the LSTM method to get better predictions of the number of positive cases of Covid -19 in Indonesia. Based on the comparison results of the RMSE and MAPE values on the ARIMA and LSTM models, it showed that the LSTM model is better than ARIMA.

摘要

自新冠疫情出现以来，随着新变种的出现，新冠疫情状况多次起伏。因此，变化迅速且极端。如果新冠阳性病例数超出医疗能力，将会出现不平等现象。所以，预测新冠阳性病例数对于避免这种情况很重要。本研究的目的是使用自回归积分移动平均（ARIMA）和长短期记忆网络（LSTM）方法预测印度尼西亚的新冠阳性病例数。将这两种方法进行比较，以获得预测印度尼西亚新冠阳性病例数的最佳方法。本研究使用的数据是2020年至2022年印度尼西亚的新冠阳性病例数。基于ARIMA建模结果，显示新冠-19阳性病例数的预测结果仍然不佳。这是因为所产生的ARIMA模型不符合假设。因此，使用LSTM方法进行建模，以更好地预测印度尼西亚的新冠阳性病例数。基于ARIMA和LSTM模型的均方根误差（RMSE）和平均绝对百分比误差（MAPE）值的比较结果，表明LSTM模型优于ARIMA。

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Long short-term memory.长短期记忆

Neural Comput. 1997 Nov 15;9(8):1735-80. doi: 10.1162/neco.1997.9.8.1735.

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