Time series forecasting methods play critical role in estimating the spread of an epidemic. The coronavirus outbreak of December 2019 has already infected millions all over the world and continues to spread on. Just when the curve of the outbreak had started to flatten, many countries have again started to witness a rise in cases which is now being referred as the 2nd wave of the pandemic. A thorough analysis of time-series forecasting models is therefore required to equip state authorities and health officials with immediate strategies for future times. This aims of the study are three-fold: (a) To model the overall trend of the spread; (b) To generate a short-term forecast of 10 days in countries with the highest incidence of confirmed cases (USA, India and Brazil); (c) To quantitatively determine the algorithm that is best suited for precise modelling of the linear and non-linear features of the time series. The comparison of forecasting models for the total cumulative cases of each country is carried out by comparing the reported data and the predicted value, and then ranking the algorithms (Prophet, Holt-Winters, LSTM, ARIMA, and ARIMA-NARNN) based on their RMSE, MAE and MAPE values. The hybrid combination of ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the best result among the selected models with a reduced RMSE, which proved to be almost 35.3% better than one of the most prevalent method of time-series prediction (ARIMA). The results demonstrated the efficacy of the hybrid implementation of the ARIMA-NARNN model over other forecasting methods such as Prophet, Holt Winters, LSTM, and the ARIMA model in encapsulating the linear as well as non-linear patterns of the epidemical datasets.

中文翻译：

---

传统时间序列模型和混合时间序列模型在预测COVID-19病例中的比较

时间序列预测方法在估计流行病的传播中起着至关重要的作用。2019年12月的冠状病毒爆发已经感染了全世界数百万人，并继续蔓延。就在爆发曲线开始趋于平缓之时，许多国家再次开始目睹病例上升，现在被称为第二次大流行。因此，需要对时间序列的预测模型进行彻底的分析，以使州政府和卫生官员具备针对未来时间的直接策略。该研究的目的是三方面的：（a）模拟传播的总体趋势；（b）对确诊病例发生率最高的国家（美国，印度和巴西）作出为期10天的短期预报；（c）定量确定最适合对时间序列的线性和非线性特征进行精确建模的算法。通过比较报告的数据和预测值，然后对算法（先知，Holt-Winters，LSTM，ARIMA和ARIMA-NARNN）进行排名，对每个国家的累计病例总数的预测模型进行比较。 RMSE，MAE和MAPE值。ARIMA和NARNN（非线性自回归神经网络）的混合组合在所选模型中具有降低的RMSE，从而获得了最佳结果，事实证明，该模型比最流行的时间序列预测方法（ARIMA）提高了近35.3％ ）。结果表明，与其他预测方法（如先知，霍尔特·温特斯，LSTM，