Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches
Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches
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DOI：
10.1016/j.chaos.2020.110015
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发表时间：
2020-09-01
影响因子：
7.8
通讯作者：
Kazancioglu, Fikret Sinasi
中科院分区：
文献类型：
--
作者：
Kirbas, Ismail;Sozen, Adnan;Kazancioglu, Fikret Sinasi
在这项研究中，丹麦、比利时、德国、法国、英国、芬兰、瑞士和土耳其的确诊COVID-19病例采用自回归综合移动平均（ARIMA）、非线性自回归神经网络（NARNN）和长短期记忆（LSTM）方法建模。使用六个模型性能指标（MSE、PSNR、RMSE、NRMSE、MAPE和SMAPE）来选择最准确的模型。根据第一步研究的结果，LSTM被认为是最准确的模型。在研究的第二阶段，提供了LSTM模型，以14天的视角进行预测，目前还不清楚。第二步研究的结果显示，许多国家的总累积病例增长率预计将略有下降。(C)2020爱思唯尔有限公司保留所有权利。
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. (C) 2020 Elsevier Ltd. All rights reserved.