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Abstract：
Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely.
We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data. Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters.
The hybrid combination displayed significant reduction in RMSE (16.23%), MAE (37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries.
Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.
We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data. Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters.
The hybrid combination displayed significant reduction in RMSE (16.23%), MAE (37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries.
Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.
摘要：
时间序列预测在大流行期间起着至关重要的作用，因为它提供了可能导致避免疾病传播的重要信息。新型冠状病毒病，COVID-19正在世界各地迅速传播。人口稠密的国家，特别是,比如印度,等待应对这一流行病的迫在眉睫的风险。正在使用不同的预测模型来预测COVID-19的未来病例。他们中的大多数人的困境是他们无法单独捕获数据的线性和非线性特征。
我们提出了一种集成自回归积分移动平均模型（ARIMA）和非线性自回归神经网络（NAR）的集成模型。ARIMA模型用于提取线性相关关系，NAR神经网络用于对包含数据非线性分量的ARIMA残差进行建模。比较：单一ARIMA模型，根据性能评估参数，比较了ARIMA-NAR模型和其他一些在不同国家的COVID-19数据上应用的现有模型。
混合组合显示RMSE显着降低（16.23％），与每日观察病例的单一ARIMA模型相比，MAE（37.89％）和MAPE（39.53％）值。对于每日报告的死亡和康复病例，也发现了类似的结果，误差百分比降低。与不同国家用于预测COVID-19的其他模型相比，我们的混合模型的RMSE值较小。
结果表明，新的混合模型在捕获COVID-19数据的线性和非线性模式方面优于单个ARIMA模型。
我们提出了一种集成自回归积分移动平均模型（ARIMA）和非线性自回归神经网络（NAR）的集成模型。ARIMA模型用于提取线性相关关系，NAR神经网络用于对包含数据非线性分量的ARIMA残差进行建模。比较：单一ARIMA模型，根据性能评估参数，比较了ARIMA-NAR模型和其他一些在不同国家的COVID-19数据上应用的现有模型。
混合组合显示RMSE显着降低（16.23％），与每日观察病例的单一ARIMA模型相比，MAE（37.89％）和MAPE（39.53％）值。对于每日报告的死亡和康复病例，也发现了类似的结果，误差百分比降低。与不同国家用于预测COVID-19的其他模型相比，我们的混合模型的RMSE值较小。
结果表明，新的混合模型在捕获COVID-19数据的线性和非线性模式方面优于单个ARIMA模型。
